Overview

Brought to you by YData

Dataset statistics

Number of variables70
Number of observations53
Missing cells59
Missing cells (%)1.6%
Duplicate rows1
Duplicate rows (%)1.9%
Total size in memory139.5 KiB
Average record size in memory2.6 KiB

Variable types

Numeric19
Categorical49
Boolean2

Alerts

osteoporosis_sec has constant value "False" Constant
alcohol has constant value "False" Constant
medicacion has constant value "si" Constant
cocina has constant value "si" Constant
baño has constant value "si" Constant
sanitario has constant value "si" Constant
continencia has constant value "si" Constant
alimentacion has constant value "si" Constant
Dataset has 1 (1.9%) duplicate rowsDuplicates
%_de_grasa is highly overall correlated with %_de_musculo and 7 other fieldsHigh correlation
%_de_musculo is highly overall correlated with %_de_grasa and 4 other fieldsHigh correlation
altura_correcta is highly overall correlated with medicamento_2High correlation
altura_en_cm is highly overall correlated with altura_mencionada and 2 other fieldsHigh correlation
altura_mencionada is highly overall correlated with altura_en_cm and 2 other fieldsHigh correlation
cadera_total is highly overall correlated with cp_sarcopenia and 5 other fieldsHigh correlation
caidas is highly overall correlated with padres_con_fractura_de_caderaHigh correlation
caminata is highly overall correlated with edad and 9 other fieldsHigh correlation
capacidad_de_usar_el_telefono is highly overall correlated with resultado_de_lawtonHigh correlation
circunferencia_de_la_pantorrilla is highly overall correlated with clas_imc and 5 other fieldsHigh correlation
clas_imc is highly overall correlated with %_de_grasa and 2 other fieldsHigh correlation
clasificacion_de_densidad_mineral is highly overall correlated with columna and 2 other fieldsHigh correlation
clasificacion_de_estado_fisico is highly overall correlated with fuerza_de_prension and 9 other fieldsHigh correlation
columna is highly overall correlated with clasificacion_de_densidad_mineralHigh correlation
compras is highly overall correlated with interpretacion_lawton and 4 other fieldsHigh correlation
cp_sarcopenia is highly overall correlated with cadera_total and 8 other fieldsHigh correlation
cuidado_del_hogar is highly overall correlated with imme and 6 other fieldsHigh correlation
edad is highly overall correlated with caminata and 3 other fieldsHigh correlation
ejercicio is highly overall correlated with fuerza and 10 other fieldsHigh correlation
estratificacion_de_riesgo is highly overall correlated with clasificacion_de_densidad_mineral and 1 other fieldsHigh correlation
finanzas is highly overall correlated with estratificacion_de_riesgo and 1 other fieldsHigh correlation
fracturas_previas is highly overall correlated with probabilida_de_fractura_de_cadera and 1 other fieldsHigh correlation
fuerza is highly overall correlated with ejercicio and 2 other fieldsHigh correlation
fuerza_de_prension is highly overall correlated with clasificacion_de_estado_fisico and 9 other fieldsHigh correlation
glucocorticoides is highly overall correlated with medicamento_2 and 1 other fieldsHigh correlation
grasa_resultado is highly overall correlated with %_de_grasa and 5 other fieldsHigh correlation
imc is highly overall correlated with %_de_grasa and 9 other fieldsHigh correlation
imme is highly overall correlated with circunferencia_de_la_pantorrilla and 6 other fieldsHigh correlation
imme_resultado is highly overall correlated with cp_sarcopenia and 4 other fieldsHigh correlation
interpretacion_lawton is highly overall correlated with compras and 2 other fieldsHigh correlation
lavanderia is highly overall correlated with medicamento_2High correlation
levantarse is highly overall correlated with ejercicio and 12 other fieldsHigh correlation
masa_muscular_absoluta is highly overall correlated with altura_en_cm and 7 other fieldsHigh correlation
medicamento_1 is highly overall correlated with altura_en_cm and 2 other fieldsHigh correlation
medicamento_2 is highly overall correlated with altura_correcta and 12 other fieldsHigh correlation
medicamentos is highly overall correlated with fuerza_de_prension and 1 other fieldsHigh correlation
nivel_de_sarcopenia is highly overall correlated with ejercicio and 5 other fieldsHigh correlation
padres_con_fractura_de_cadera is highly overall correlated with caidas and 1 other fieldsHigh correlation
peso is highly overall correlated with circunferencia_de_la_pantorrilla and 7 other fieldsHigh correlation
probabilida_de_fractura_de_cadera is highly overall correlated with cp_sarcopenia and 4 other fieldsHigh correlation
probabilida_de_fractura_por_fragilidad is highly overall correlated with edad and 3 other fieldsHigh correlation
prueba_de_la_silla is highly overall correlated with clasificacion_de_estado_fisico and 7 other fieldsHigh correlation
puntaje_de_balance is highly overall correlated with %_de_grasa and 12 other fieldsHigh correlation
puntaje_de_katz is highly overall correlated with cadera_total and 9 other fieldsHigh correlation
puntaje_de_velocidad_de_marcha is highly overall correlated with caminata and 14 other fieldsHigh correlation
puntaje_sppb is highly overall correlated with caminata and 15 other fieldsHigh correlation
resultado_de_fp is highly overall correlated with %_de_grasa and 18 other fieldsHigh correlation
resultado_de_katz is highly overall correlated with imc and 6 other fieldsHigh correlation
resultado_de_lawton is highly overall correlated with caminata and 10 other fieldsHigh correlation
resultado_de_ps is highly overall correlated with clasificacion_de_estado_fisico and 7 other fieldsHigh correlation
resultado_de_velocidad is highly overall correlated with %_de_musculo and 16 other fieldsHigh correlation
sarc_f_puntaje is highly overall correlated with caminata and 19 other fieldsHigh correlation
sarc_f_resultado is highly overall correlated with %_de_grasa and 19 other fieldsHigh correlation
sarcopenia is highly overall correlated with clasificacion_de_estado_fisico and 8 other fieldsHigh correlation
subir_escaleras is highly overall correlated with glucocorticoides and 2 other fieldsHigh correlation
tabaquismo is highly overall correlated with %_de_grasa and 2 other fieldsHigh correlation
tiempo_de_ejercicio is highly overall correlated with ejercicio and 4 other fieldsHigh correlation
transferencia is highly overall correlated with imc and 6 other fieldsHigh correlation
transporte is highly overall correlated with imc and 6 other fieldsHigh correlation
velocidad_de_la_marcha is highly overall correlated with caminata and 11 other fieldsHigh correlation
vestido is highly overall correlated with cadera_total and 5 other fieldsHigh correlation
grasa_resultado is highly imbalanced (52.1%) Imbalance
imme_resultado is highly imbalanced (53.1%) Imbalance
padres_con_fractura_de_cadera is highly imbalanced (54.9%) Imbalance
tabaquismo is highly imbalanced (61.4%) Imbalance
glucocorticoides is highly imbalanced (76.8%) Imbalance
artritis_reumatoide is highly imbalanced (61.4%) Imbalance
capacidad_de_usar_el_telefono is highly imbalanced (61.4%) Imbalance
finanzas is highly imbalanced (76.8%) Imbalance
cuidado_del_hogar is highly imbalanced (68.6%) Imbalance
lavanderia is highly imbalanced (86.5%) Imbalance
vestido is highly imbalanced (86.5%) Imbalance
puntaje_de_katz is highly imbalanced (63.2%) Imbalance
prueba_de_la_silla has 1 (1.9%) missing values Missing
medicamento_1 has 14 (26.4%) missing values Missing
medicamento_2 has 44 (83.0%) missing values Missing
sarc_f_puntaje has 13 (24.5%) zeros Zeros

Reproduction

Analysis started2025-05-02 19:21:47.652538
Analysis finished2025-05-02 19:22:05.294393
Duration17.64 seconds
Software versionydata-profiling v4.16.1
Download configurationconfig.json

Variables

edad
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.490566
Minimum55
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:05.327385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile56.6
Q162
median65
Q370
95-th percentile78.4
Maximum80
Range25
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.5533979
Coefficient of variation (CV)0.09856132
Kurtosis-0.54455153
Mean66.490566
Median Absolute Deviation (MAD)4
Skewness0.44032168
Sum3524
Variance42.947025
MonotonicityNot monotonic
2025-05-02T12:22:05.376479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
64 6
 
11.3%
62 5
 
9.4%
65 4
 
7.5%
70 3
 
5.7%
60 3
 
5.7%
59 3
 
5.7%
68 3
 
5.7%
73 3
 
5.7%
67 3
 
5.7%
76 2
 
3.8%
Other values (13) 18
34.0%
ValueCountFrequency (%)
55 1
 
1.9%
56 2
 
3.8%
57 1
 
1.9%
59 3
5.7%
60 3
5.7%
61 1
 
1.9%
62 5
9.4%
63 2
 
3.8%
64 6
11.3%
65 4
7.5%
ValueCountFrequency (%)
80 2
3.8%
79 1
 
1.9%
78 2
3.8%
76 2
3.8%
75 1
 
1.9%
74 1
 
1.9%
73 3
5.7%
71 1
 
1.9%
70 3
5.7%
69 1
 
1.9%

peso
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.745283
Minimum46
Maximum104.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:05.427268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile52.24
Q161.4
median67.9
Q380.9
95-th percentile91
Maximum104.4
Range58.4
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation12.51503
Coefficient of variation (CV)0.17690268
Kurtosis0.041730874
Mean70.745283
Median Absolute Deviation (MAD)7.9
Skewness0.42770727
Sum3749.5
Variance156.62599
MonotonicityNot monotonic
2025-05-02T12:22:05.480773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
67.6 2
 
3.8%
85.4 2
 
3.8%
65 2
 
3.8%
60 2
 
3.8%
61 2
 
3.8%
67.2 1
 
1.9%
64.1 1
 
1.9%
73.3 1
 
1.9%
63.8 1
 
1.9%
69.7 1
 
1.9%
Other values (38) 38
71.7%
ValueCountFrequency (%)
46 1
1.9%
48 1
1.9%
49 1
1.9%
54.4 1
1.9%
56 1
1.9%
57.7 1
1.9%
59.3 1
1.9%
59.7 1
1.9%
60 2
3.8%
60.3 1
1.9%
ValueCountFrequency (%)
104.4 1
1.9%
97.8 1
1.9%
92.2 1
1.9%
90.2 1
1.9%
90 1
1.9%
85.4 2
3.8%
84.5 1
1.9%
82.5 1
1.9%
82.4 1
1.9%
81.9 1
1.9%

altura_en_cm
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.9434
Minimum137
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:05.529497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum137
5-th percentile143
Q1150
median154
Q3159
95-th percentile165.6
Maximum173
Range36
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.2653421
Coefficient of variation (CV)0.047194893
Kurtosis0.19446426
Mean153.9434
Median Absolute Deviation (MAD)5
Skewness0.2473795
Sum8159
Variance52.785196
MonotonicityNot monotonic
2025-05-02T12:22:05.579279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
152 5
 
9.4%
155 5
 
9.4%
154 4
 
7.5%
160 4
 
7.5%
150 3
 
5.7%
147 3
 
5.7%
151 3
 
5.7%
159 2
 
3.8%
157 2
 
3.8%
144 2
 
3.8%
Other values (14) 20
37.7%
ValueCountFrequency (%)
137 1
 
1.9%
142 1
 
1.9%
143 2
 
3.8%
144 2
 
3.8%
146 2
 
3.8%
147 3
5.7%
149 2
 
3.8%
150 3
5.7%
151 3
5.7%
152 5
9.4%
ValueCountFrequency (%)
173 1
 
1.9%
169 1
 
1.9%
168 1
 
1.9%
164 2
3.8%
163 2
3.8%
162 1
 
1.9%
160 4
7.5%
159 2
3.8%
158 1
 
1.9%
157 2
3.8%

altura_mencionada
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.20755
Minimum140
Maximum170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:05.626809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum140
5-th percentile145
Q1152
median155
Q3162
95-th percentile168
Maximum170
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.3546388
Coefficient of variation (CV)0.04708248
Kurtosis-0.44472063
Mean156.20755
Median Absolute Deviation (MAD)5
Skewness-0.034099859
Sum8279
Variance54.090711
MonotonicityNot monotonic
2025-05-02T12:22:05.673550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
152 6
 
11.3%
155 5
 
9.4%
154 5
 
9.4%
160 5
 
9.4%
147 3
 
5.7%
158 3
 
5.7%
168 3
 
5.7%
162 3
 
5.7%
140 2
 
3.8%
145 2
 
3.8%
Other values (12) 16
30.2%
ValueCountFrequency (%)
140 2
 
3.8%
145 2
 
3.8%
147 3
5.7%
148 1
 
1.9%
149 1
 
1.9%
150 1
 
1.9%
151 2
 
3.8%
152 6
11.3%
154 5
9.4%
155 5
9.4%
ValueCountFrequency (%)
170 1
 
1.9%
169 1
 
1.9%
168 3
5.7%
167 1
 
1.9%
166 2
 
3.8%
165 1
 
1.9%
163 2
 
3.8%
162 3
5.7%
160 5
9.4%
158 3
5.7%

altura_correcta
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
no
45 
si

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 45
84.9%
si 8
 
15.1%

Length

2025-05-02T12:22:05.722126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:05.760934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 45
84.9%
si 8
 
15.1%

Most occurring characters

ValueCountFrequency (%)
n 45
42.5%
o 45
42.5%
s 8
 
7.5%
i 8
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 45
42.5%
o 45
42.5%
s 8
 
7.5%
i 8
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 45
42.5%
o 45
42.5%
s 8
 
7.5%
i 8
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 45
42.5%
o 45
42.5%
s 8
 
7.5%
i 8
 
7.5%

imc
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.184906
Minimum20.3
Maximum46.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:05.812955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20.3
5-th percentile24.52
Q127
median30.1
Q334.6
95-th percentile45.04
Maximum46.3
Range26
Interquartile range (IQR)7.6

Descriptive statistics

Standard deviation5.9150524
Coefficient of variation (CV)0.18967678
Kurtosis0.6808555
Mean31.184906
Median Absolute Deviation (MAD)3.7
Skewness0.96087591
Sum1652.8
Variance34.987845
MonotonicityNot monotonic
2025-05-02T12:22:05.871294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
29.1 3
 
5.7%
26.4 2
 
3.8%
30.6 2
 
3.8%
25.9 2
 
3.8%
45.7 2
 
3.8%
27.9 2
 
3.8%
37.5 1
 
1.9%
25.4 1
 
1.9%
24.1 1
 
1.9%
30.3 1
 
1.9%
Other values (36) 36
67.9%
ValueCountFrequency (%)
20.3 1
1.9%
22.7 1
1.9%
24.1 1
1.9%
24.8 1
1.9%
25.4 1
1.9%
25.7 1
1.9%
25.9 2
3.8%
26 1
1.9%
26.1 1
1.9%
26.4 2
3.8%
ValueCountFrequency (%)
46.3 1
1.9%
45.7 2
3.8%
44.6 1
1.9%
40.3 1
1.9%
38 1
1.9%
37.5 1
1.9%
37 1
1.9%
36.5 1
1.9%
35.9 1
1.9%
35.7 1
1.9%

clas_imc
Categorical

High correlation 

Distinct5
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
sobrepeso
22 
obesidad 1
14 
obesidad 2
obesidad 3
adecuado

Length

Max length10
Median length10
Mean length9.4339623
Min length8

Characters and Unicode

Total characters500
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowobesidad 1
2nd rowsobrepeso
3rd rowsobrepeso
4th rowsobrepeso
5th rowobesidad 2

Common Values

ValueCountFrequency (%)
sobrepeso 22
41.5%
obesidad 1 14
26.4%
obesidad 2 8
 
15.1%
obesidad 3 5
 
9.4%
adecuado 4
 
7.5%

Length

2025-05-02T12:22:05.928272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:05.976751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
obesidad 27
33.8%
sobrepeso 22
27.5%
1 14
17.5%
2 8
 
10.0%
3 5
 
6.2%
adecuado 4
 
5.0%

Most occurring characters

ValueCountFrequency (%)
o 75
15.0%
e 75
15.0%
s 71
14.2%
d 62
12.4%
b 49
9.8%
a 35
7.0%
i 27
 
5.4%
27
 
5.4%
r 22
 
4.4%
p 22
 
4.4%
Other values (5) 35
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 75
15.0%
e 75
15.0%
s 71
14.2%
d 62
12.4%
b 49
9.8%
a 35
7.0%
i 27
 
5.4%
27
 
5.4%
r 22
 
4.4%
p 22
 
4.4%
Other values (5) 35
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 75
15.0%
e 75
15.0%
s 71
14.2%
d 62
12.4%
b 49
9.8%
a 35
7.0%
i 27
 
5.4%
27
 
5.4%
r 22
 
4.4%
p 22
 
4.4%
Other values (5) 35
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 75
15.0%
e 75
15.0%
s 71
14.2%
d 62
12.4%
b 49
9.8%
a 35
7.0%
i 27
 
5.4%
27
 
5.4%
r 22
 
4.4%
p 22
 
4.4%
Other values (5) 35
7.0%

circunferencia_de_la_pantorrilla
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.881132
Minimum28
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:06.023696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile29.2
Q133
median36
Q338
95-th percentile43.4
Maximum46
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.1033315
Coefficient of variation (CV)0.11435903
Kurtosis0.024320267
Mean35.881132
Median Absolute Deviation (MAD)2
Skewness0.3612294
Sum1901.7
Variance16.837329
MonotonicityNot monotonic
2025-05-02T12:22:06.073834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
34 7
13.2%
36 5
 
9.4%
35 4
 
7.5%
33 4
 
7.5%
36.5 3
 
5.7%
32.5 3
 
5.7%
28 3
 
5.7%
38 3
 
5.7%
37 3
 
5.7%
31 2
 
3.8%
Other values (12) 16
30.2%
ValueCountFrequency (%)
28 3
5.7%
30 1
 
1.9%
31 2
 
3.8%
32 1
 
1.9%
32.5 3
5.7%
33 4
7.5%
33.5 1
 
1.9%
34 7
13.2%
35 4
7.5%
36 5
9.4%
ValueCountFrequency (%)
46 1
 
1.9%
44 2
3.8%
43 1
 
1.9%
42 2
3.8%
41.2 1
 
1.9%
41 2
3.8%
40 1
 
1.9%
39 1
 
1.9%
38 3
5.7%
37.5 2
3.8%

cp_sarcopenia
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
no
46 
si

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 46
86.8%
si 7
 
13.2%

Length

2025-05-02T12:22:06.125020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:06.162932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 46
86.8%
si 7
 
13.2%

Most occurring characters

ValueCountFrequency (%)
n 46
43.4%
o 46
43.4%
s 7
 
6.6%
i 7
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 46
43.4%
o 46
43.4%
s 7
 
6.6%
i 7
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 46
43.4%
o 46
43.4%
s 7
 
6.6%
i 7
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 46
43.4%
o 46
43.4%
s 7
 
6.6%
i 7
 
6.6%

%_de_grasa
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.89434
Minimum23.4
Maximum57.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:06.205879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23.4
5-th percentile29.12
Q138.4
median41.2
Q346.6
95-th percentile54.54
Maximum57.4
Range34
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation7.3904672
Coefficient of variation (CV)0.1764073
Kurtosis0.15862211
Mean41.89434
Median Absolute Deviation (MAD)4.9
Skewness-0.075892754
Sum2220.4
Variance54.619006
MonotonicityNot monotonic
2025-05-02T12:22:06.261115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
33.7 2
 
3.8%
41.9 2
 
3.8%
46.6 2
 
3.8%
46.1 2
 
3.8%
38.8 2
 
3.8%
57 2
 
3.8%
40.8 1
 
1.9%
46.7 1
 
1.9%
29 1
 
1.9%
43 1
 
1.9%
Other values (37) 37
69.8%
ValueCountFrequency (%)
23.4 1
1.9%
26.6 1
1.9%
29 1
1.9%
29.2 1
1.9%
32.8 1
1.9%
33.7 2
3.8%
34.4 1
1.9%
34.6 1
1.9%
35 1
1.9%
35.8 1
1.9%
ValueCountFrequency (%)
57.4 1
1.9%
57 2
3.8%
52.9 1
1.9%
51.8 1
1.9%
51.5 1
1.9%
49.7 1
1.9%
49.3 1
1.9%
48.3 1
1.9%
47.9 1
1.9%
47.7 1
1.9%

grasa_resultado
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
peligrosamente alta
43 
alta
bueno
 
2
normal
 
2

Length

Max length19
Median length19
Mean length16.283019
Min length4

Characters and Unicode

Total characters863
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpeligrosamente alta
2nd rowpeligrosamente alta
3rd rowalta
4th rowpeligrosamente alta
5th rowpeligrosamente alta

Common Values

ValueCountFrequency (%)
peligrosamente alta 43
81.1%
alta 6
 
11.3%
bueno 2
 
3.8%
normal 2
 
3.8%

Length

2025-05-02T12:22:06.318446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:06.362412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
alta 49
51.0%
peligrosamente 43
44.8%
bueno 2
 
2.1%
normal 2
 
2.1%

Most occurring characters

ValueCountFrequency (%)
a 143
16.6%
e 131
15.2%
l 94
10.9%
t 92
10.7%
o 47
 
5.4%
n 47
 
5.4%
r 45
 
5.2%
m 45
 
5.2%
p 43
 
5.0%
i 43
 
5.0%
Other values (5) 133
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 143
16.6%
e 131
15.2%
l 94
10.9%
t 92
10.7%
o 47
 
5.4%
n 47
 
5.4%
r 45
 
5.2%
m 45
 
5.2%
p 43
 
5.0%
i 43
 
5.0%
Other values (5) 133
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 143
16.6%
e 131
15.2%
l 94
10.9%
t 92
10.7%
o 47
 
5.4%
n 47
 
5.4%
r 45
 
5.2%
m 45
 
5.2%
p 43
 
5.0%
i 43
 
5.0%
Other values (5) 133
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 143
16.6%
e 131
15.2%
l 94
10.9%
t 92
10.7%
o 47
 
5.4%
n 47
 
5.4%
r 45
 
5.2%
m 45
 
5.2%
p 43
 
5.0%
i 43
 
5.0%
Other values (5) 133
15.4%

%_de_musculo
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.841509
Minimum19
Maximum31.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:06.410307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile19.1
Q122.7
median25.1
Q326.7
95-th percentile29.36
Maximum31.1
Range12.1
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0576785
Coefficient of variation (CV)0.12308747
Kurtosis-0.51285156
Mean24.841509
Median Absolute Deviation (MAD)2.3
Skewness0.012158714
Sum1316.6
Variance9.3493977
MonotonicityNot monotonic
2025-05-02T12:22:06.464557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
19.1 3
 
5.7%
22.5 3
 
5.7%
26.5 2
 
3.8%
22.9 2
 
3.8%
25.7 2
 
3.8%
29 2
 
3.8%
22.7 2
 
3.8%
31.1 2
 
3.8%
25.1 2
 
3.8%
24.4 2
 
3.8%
Other values (30) 31
58.5%
ValueCountFrequency (%)
19 1
 
1.9%
19.1 3
5.7%
21 1
 
1.9%
21.1 1
 
1.9%
21.3 1
 
1.9%
21.9 1
 
1.9%
22.1 1
 
1.9%
22.2 1
 
1.9%
22.5 3
5.7%
22.7 2
3.8%
ValueCountFrequency (%)
31.1 2
3.8%
29.9 1
1.9%
29 2
3.8%
28.9 1
1.9%
28.6 2
3.8%
28.5 1
1.9%
28.1 1
1.9%
27.8 1
1.9%
27 1
1.9%
26.8 1
1.9%

masa_muscular_absoluta
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.051698
Minimum8.74
Maximum30.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:06.519929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.74
5-th percentile13.592
Q116.07
median17.94
Q320.4
95-th percentile22.404
Maximum30.42
Range21.68
Interquartile range (IQR)4.33

Descriptive statistics

Standard deviation3.4723605
Coefficient of variation (CV)0.19235645
Kurtosis2.4508654
Mean18.051698
Median Absolute Deviation (MAD)2.23
Skewness0.37255499
Sum956.74
Variance12.057287
MonotonicityNot monotonic
2025-05-02T12:22:06.576296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.95 2
 
3.8%
21.7 1
 
1.9%
17.24 1
 
1.9%
18.56 1
 
1.9%
19.94 1
 
1.9%
15.07 1
 
1.9%
14.18 1
 
1.9%
16.4 1
 
1.9%
19.93 1
 
1.9%
30.42 1
 
1.9%
Other values (42) 42
79.2%
ValueCountFrequency (%)
8.74 1
1.9%
10.83 1
1.9%
13.1 1
1.9%
13.92 1
1.9%
14.18 1
1.9%
14.22 1
1.9%
14.53 1
1.9%
14.65 1
1.9%
14.68 1
1.9%
15.07 1
1.9%
ValueCountFrequency (%)
30.42 1
1.9%
23.18 1
1.9%
22.65 1
1.9%
22.24 1
1.9%
21.7 1
1.9%
21.41 1
1.9%
21.02 1
1.9%
20.95 2
3.8%
20.85 1
1.9%
20.79 1
1.9%

imme
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6192453
Minimum4.04
Maximum12.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:06.630331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.04
5-th percentile5.86
Q17.09
median7.53
Q38.17
95-th percentile9.424
Maximum12.36
Range8.32
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation1.2144148
Coefficient of variation (CV)0.15938781
Kurtosis4.3886087
Mean7.6192453
Median Absolute Deviation (MAD)0.59
Skewness0.68622641
Sum403.82
Variance1.4748033
MonotonicityNot monotonic
2025-05-02T12:22:06.683845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
5.9 2
 
3.8%
7.15 2
 
3.8%
8.17 2
 
3.8%
7.49 2
 
3.8%
7.69 2
 
3.8%
8.72 2
 
3.8%
7.28 2
 
3.8%
7.94 1
 
1.9%
7 1
 
1.9%
7.5 1
 
1.9%
Other values (36) 36
67.9%
ValueCountFrequency (%)
4.04 1
1.9%
5.79 1
1.9%
5.8 1
1.9%
5.9 2
3.8%
6.34 1
1.9%
6.58 1
1.9%
6.69 1
1.9%
6.75 1
1.9%
6.78 1
1.9%
6.8 1
1.9%
ValueCountFrequency (%)
12.36 1
1.9%
9.5 1
1.9%
9.43 1
1.9%
9.42 1
1.9%
9.02 1
1.9%
8.86 1
1.9%
8.85 1
1.9%
8.72 2
3.8%
8.7 1
1.9%
8.28 1
1.9%

imme_resultado
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
normal
44 
sarcopenia grado 1
sarcopenia grado 2
 
1

Length

Max length18
Median length6
Mean length8.0377358
Min length6

Characters and Unicode

Total characters426
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 44
83.0%
sarcopenia grado 1 8
 
15.1%
sarcopenia grado 2 1
 
1.9%

Length

2025-05-02T12:22:06.735053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:06.776387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 44
62.0%
sarcopenia 9
 
12.7%
grado 9
 
12.7%
1 8
 
11.3%
2 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
a 71
16.7%
o 62
14.6%
r 62
14.6%
n 53
12.4%
m 44
10.3%
l 44
10.3%
18
 
4.2%
s 9
 
2.1%
c 9
 
2.1%
p 9
 
2.1%
Other values (6) 45
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 71
16.7%
o 62
14.6%
r 62
14.6%
n 53
12.4%
m 44
10.3%
l 44
10.3%
18
 
4.2%
s 9
 
2.1%
c 9
 
2.1%
p 9
 
2.1%
Other values (6) 45
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 71
16.7%
o 62
14.6%
r 62
14.6%
n 53
12.4%
m 44
10.3%
l 44
10.3%
18
 
4.2%
s 9
 
2.1%
c 9
 
2.1%
p 9
 
2.1%
Other values (6) 45
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 71
16.7%
o 62
14.6%
r 62
14.6%
n 53
12.4%
m 44
10.3%
l 44
10.3%
18
 
4.2%
s 9
 
2.1%
c 9
 
2.1%
p 9
 
2.1%
Other values (6) 45
10.6%

cadera_total
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.4566038
Minimum-4.2
Maximum0.5
Zeros0
Zeros (%)0.0%
Negative51
Negative (%)96.2%
Memory size556.0 B
2025-05-02T12:22:06.819816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-4.2
5-th percentile-2.72
Q1-2
median-1.5
Q3-0.8
95-th percentile-0.16
Maximum0.5
Range4.7
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.86213274
Coefficient of variation (CV)-0.59187869
Kurtosis1.3385586
Mean-1.4566038
Median Absolute Deviation (MAD)0.5
Skewness-0.27311317
Sum-77.2
Variance0.74327286
MonotonicityNot monotonic
2025-05-02T12:22:06.866228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
-1.3 6
 
11.3%
-1.5 6
 
11.3%
-2 4
 
7.5%
-2.1 4
 
7.5%
-2.3 3
 
5.7%
-0.6 3
 
5.7%
-0.7 3
 
5.7%
-1.7 3
 
5.7%
-1.1 2
 
3.8%
-0.8 2
 
3.8%
Other values (15) 17
32.1%
ValueCountFrequency (%)
-4.2 1
 
1.9%
-3.2 1
 
1.9%
-2.9 1
 
1.9%
-2.6 1
 
1.9%
-2.3 3
5.7%
-2.2 1
 
1.9%
-2.1 4
7.5%
-2 4
7.5%
-1.9 1
 
1.9%
-1.8 1
 
1.9%
ValueCountFrequency (%)
0.5 2
3.8%
-0.1 1
 
1.9%
-0.2 1
 
1.9%
-0.3 1
 
1.9%
-0.5 1
 
1.9%
-0.6 3
5.7%
-0.7 3
5.7%
-0.8 2
3.8%
-1.1 2
3.8%
-1.2 2
3.8%

columna
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.9528302
Minimum-3.8
Maximum0.5
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)98.1%
Memory size556.0 B
2025-05-02T12:22:06.912007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.8
5-th percentile-3.18
Q1-2.5
median-1.9
Q3-1.3
95-th percentile-1.1
Maximum0.5
Range4.3
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.76876619
Coefficient of variation (CV)-0.39366771
Kurtosis1.0456548
Mean-1.9528302
Median Absolute Deviation (MAD)0.6
Skewness0.12468236
Sum-103.5
Variance0.59100145
MonotonicityNot monotonic
2025-05-02T12:22:06.957214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
-1.3 7
13.2%
-2.5 5
 
9.4%
-1.6 4
 
7.5%
-1.2 4
 
7.5%
-2.8 3
 
5.7%
-1.9 3
 
5.7%
-2.2 3
 
5.7%
-2.3 3
 
5.7%
-1.1 2
 
3.8%
-2.4 2
 
3.8%
Other values (12) 17
32.1%
ValueCountFrequency (%)
-3.8 1
 
1.9%
-3.5 1
 
1.9%
-3.3 1
 
1.9%
-3.1 2
 
3.8%
-2.8 3
5.7%
-2.7 1
 
1.9%
-2.5 5
9.4%
-2.4 2
 
3.8%
-2.3 3
5.7%
-2.2 3
5.7%
ValueCountFrequency (%)
0.5 1
 
1.9%
-0.8 1
 
1.9%
-1.1 2
 
3.8%
-1.2 4
7.5%
-1.3 7
13.2%
-1.4 2
 
3.8%
-1.6 4
7.5%
-1.7 1
 
1.9%
-1.8 2
 
3.8%
-1.9 3
5.7%

clasificacion_de_densidad_mineral
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
osteopenia
35 
osteoporosis
18 

Length

Max length12
Median length10
Mean length10.679245
Min length10

Characters and Unicode

Total characters566
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowosteopenia
2nd rowosteoporosis
3rd rowosteoporosis
4th rowosteoporosis
5th rowosteopenia

Common Values

ValueCountFrequency (%)
osteopenia 35
66.0%
osteoporosis 18
34.0%

Length

2025-05-02T12:22:07.011007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:07.254995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
osteopenia 35
66.0%
osteoporosis 18
34.0%

Most occurring characters

ValueCountFrequency (%)
o 142
25.1%
s 89
15.7%
e 88
15.5%
t 53
 
9.4%
p 53
 
9.4%
i 53
 
9.4%
n 35
 
6.2%
a 35
 
6.2%
r 18
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 142
25.1%
s 89
15.7%
e 88
15.5%
t 53
 
9.4%
p 53
 
9.4%
i 53
 
9.4%
n 35
 
6.2%
a 35
 
6.2%
r 18
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 142
25.1%
s 89
15.7%
e 88
15.5%
t 53
 
9.4%
p 53
 
9.4%
i 53
 
9.4%
n 35
 
6.2%
a 35
 
6.2%
r 18
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 142
25.1%
s 89
15.7%
e 88
15.5%
t 53
 
9.4%
p 53
 
9.4%
i 53
 
9.4%
n 35
 
6.2%
a 35
 
6.2%
r 18
 
3.2%

fracturas_previas
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
no
37 
si
16 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowsi
4th rowsi
5th rowsi

Common Values

ValueCountFrequency (%)
no 37
69.8%
si 16
30.2%

Length

2025-05-02T12:22:07.297186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:07.335141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 37
69.8%
si 16
30.2%

Most occurring characters

ValueCountFrequency (%)
n 37
34.9%
o 37
34.9%
s 16
15.1%
i 16
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 37
34.9%
o 37
34.9%
s 16
15.1%
i 16
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 37
34.9%
o 37
34.9%
s 16
15.1%
i 16
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 37
34.9%
o 37
34.9%
s 16
15.1%
i 16
15.1%

padres_con_fractura_de_cadera
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
no
48 
si

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 48
90.6%
si 5
 
9.4%

Length

2025-05-02T12:22:07.381472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:07.420305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 48
90.6%
si 5
 
9.4%

Most occurring characters

ValueCountFrequency (%)
n 48
45.3%
o 48
45.3%
s 5
 
4.7%
i 5
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 48
45.3%
o 48
45.3%
s 5
 
4.7%
i 5
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 48
45.3%
o 48
45.3%
s 5
 
4.7%
i 5
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 48
45.3%
o 48
45.3%
s 5
 
4.7%
i 5
 
4.7%

tabaquismo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
no
49 
si
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 49
92.5%
si 4
 
7.5%

Length

2025-05-02T12:22:07.461326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:07.498621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 49
92.5%
si 4
 
7.5%

Most occurring characters

ValueCountFrequency (%)
n 49
46.2%
o 49
46.2%
s 4
 
3.8%
i 4
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 49
46.2%
o 49
46.2%
s 4
 
3.8%
i 4
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 49
46.2%
o 49
46.2%
s 4
 
3.8%
i 4
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 49
46.2%
o 49
46.2%
s 4
 
3.8%
i 4
 
3.8%

glucocorticoides
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
no
51 
si
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowsi
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 51
96.2%
si 2
 
3.8%

Length

2025-05-02T12:22:07.540637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:07.578466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 51
96.2%
si 2
 
3.8%

Most occurring characters

ValueCountFrequency (%)
n 51
48.1%
o 51
48.1%
s 2
 
1.9%
i 2
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 51
48.1%
o 51
48.1%
s 2
 
1.9%
i 2
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 51
48.1%
o 51
48.1%
s 2
 
1.9%
i 2
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 51
48.1%
o 51
48.1%
s 2
 
1.9%
i 2
 
1.9%

artritis_reumatoide
Categorical

Imbalance 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
no
49 
si
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 49
92.5%
si 4
 
7.5%

Length

2025-05-02T12:22:07.618495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:07.655532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 49
92.5%
si 4
 
7.5%

Most occurring characters

ValueCountFrequency (%)
n 49
46.2%
o 49
46.2%
s 4
 
3.8%
i 4
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 49
46.2%
o 49
46.2%
s 4
 
3.8%
i 4
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 49
46.2%
o 49
46.2%
s 4
 
3.8%
i 4
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 49
46.2%
o 49
46.2%
s 4
 
3.8%
i 4
 
3.8%

osteoporosis_sec
Boolean

Constant 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size185.0 B
False
53 
ValueCountFrequency (%)
False 53
100.0%
2025-05-02T12:22:07.688377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

alcohol
Boolean

Constant 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size185.0 B
False
53 
ValueCountFrequency (%)
False 53
100.0%
2025-05-02T12:22:07.718855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

probabilida_de_fractura_por_fragilidad
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6886792
Minimum2.7
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:07.759054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile3.1
Q14.2
median6.4
Q38.8
95-th percentile18
Maximum21
Range18.3
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation4.7938125
Coefficient of variation (CV)0.62348973
Kurtosis0.82467907
Mean7.6886792
Median Absolute Deviation (MAD)2.2
Skewness1.3098588
Sum407.5
Variance22.980639
MonotonicityNot monotonic
2025-05-02T12:22:07.813805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3.1 3
 
5.7%
3.2 2
 
3.8%
16 2
 
3.8%
4.2 2
 
3.8%
8.1 2
 
3.8%
4.6 2
 
3.8%
4.7 2
 
3.8%
3.8 2
 
3.8%
18 2
 
3.8%
7.2 2
 
3.8%
Other values (30) 32
60.4%
ValueCountFrequency (%)
2.7 1
 
1.9%
3.1 3
5.7%
3.2 2
3.8%
3.6 1
 
1.9%
3.7 1
 
1.9%
3.8 2
3.8%
4 1
 
1.9%
4.1 1
 
1.9%
4.2 2
3.8%
4.3 2
3.8%
ValueCountFrequency (%)
21 1
1.9%
20 1
1.9%
18 2
3.8%
16 2
3.8%
15 1
1.9%
14 1
1.9%
13 2
3.8%
11 1
1.9%
9.4 1
1.9%
8.9 1
1.9%

probabilida_de_fractura_de_cadera
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2811321
Minimum0.3
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:07.866131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.4
Q10.6
median1.1
Q32.5
95-th percentile8.42
Maximum12
Range11.7
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation2.8206694
Coefficient of variation (CV)1.2365217
Kurtosis5.3406269
Mean2.2811321
Median Absolute Deviation (MAD)0.6
Skewness2.3635216
Sum120.9
Variance7.9561756
MonotonicityNot monotonic
2025-05-02T12:22:07.919274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.6 6
 
11.3%
0.5 5
 
9.4%
0.4 4
 
7.5%
0.9 4
 
7.5%
0.7 3
 
5.7%
2.7 3
 
5.7%
2 2
 
3.8%
12 2
 
3.8%
0.8 2
 
3.8%
1.1 2
 
3.8%
Other values (17) 20
37.7%
ValueCountFrequency (%)
0.3 1
 
1.9%
0.4 4
7.5%
0.5 5
9.4%
0.6 6
11.3%
0.7 3
5.7%
0.8 2
 
3.8%
0.9 4
7.5%
1.1 2
 
3.8%
1.3 1
 
1.9%
1.4 1
 
1.9%
ValueCountFrequency (%)
12 2
3.8%
11 1
 
1.9%
6.7 1
 
1.9%
6.6 2
3.8%
5 1
 
1.9%
4.3 1
 
1.9%
4 1
 
1.9%
2.8 1
 
1.9%
2.7 3
5.7%
2.5 1
 
1.9%

estratificacion_de_riesgo
Categorical

High correlation 

Distinct4
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
moderada
29 
alta
10 
muy alta
baja

Length

Max length8
Median length8
Mean length6.8679245
Min length4

Characters and Unicode

Total characters364
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbaja
2nd rowalta
3rd rowmuy alta
4th rowmuy alta
5th rowmuy alta

Common Values

ValueCountFrequency (%)
moderada 29
54.7%
alta 10
 
18.9%
muy alta 9
 
17.0%
baja 5
 
9.4%

Length

2025-05-02T12:22:07.975999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:08.022953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
moderada 29
46.8%
alta 19
30.6%
muy 9
 
14.5%
baja 5
 
8.1%

Most occurring characters

ValueCountFrequency (%)
a 106
29.1%
d 58
15.9%
m 38
 
10.4%
o 29
 
8.0%
e 29
 
8.0%
r 29
 
8.0%
l 19
 
5.2%
t 19
 
5.2%
u 9
 
2.5%
y 9
 
2.5%
Other values (3) 19
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 106
29.1%
d 58
15.9%
m 38
 
10.4%
o 29
 
8.0%
e 29
 
8.0%
r 29
 
8.0%
l 19
 
5.2%
t 19
 
5.2%
u 9
 
2.5%
y 9
 
2.5%
Other values (3) 19
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 106
29.1%
d 58
15.9%
m 38
 
10.4%
o 29
 
8.0%
e 29
 
8.0%
r 29
 
8.0%
l 19
 
5.2%
t 19
 
5.2%
u 9
 
2.5%
y 9
 
2.5%
Other values (3) 19
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 106
29.1%
d 58
15.9%
m 38
 
10.4%
o 29
 
8.0%
e 29
 
8.0%
r 29
 
8.0%
l 19
 
5.2%
t 19
 
5.2%
u 9
 
2.5%
y 9
 
2.5%
Other values (3) 19
 
5.2%

fuerza_de_prension
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.618868
Minimum13
Maximum31.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:08.072441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15.26
Q118.7
median21.7
Q324.4
95-th percentile29.52
Maximum31.9
Range18.9
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.4188432
Coefficient of variation (CV)0.20439753
Kurtosis-0.37258812
Mean21.618868
Median Absolute Deviation (MAD)2.9
Skewness0.33743654
Sum1145.8
Variance19.526176
MonotonicityNot monotonic
2025-05-02T12:22:08.130720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
21.9 2
 
3.8%
23.1 2
 
3.8%
22 2
 
3.8%
19.6 2
 
3.8%
18.8 2
 
3.8%
25.2 2
 
3.8%
20.6 1
 
1.9%
31.9 1
 
1.9%
26.4 1
 
1.9%
21.5 1
 
1.9%
Other values (37) 37
69.8%
ValueCountFrequency (%)
13 1
1.9%
14.1 1
1.9%
14.6 1
1.9%
15.7 1
1.9%
16.2 1
1.9%
16.4 1
1.9%
16.7 1
1.9%
16.9 1
1.9%
17.1 1
1.9%
17.5 1
1.9%
ValueCountFrequency (%)
31.9 1
1.9%
30.7 1
1.9%
29.7 1
1.9%
29.4 1
1.9%
29.1 1
1.9%
28 1
1.9%
27 1
1.9%
26.4 1
1.9%
25.2 2
3.8%
25.1 1
1.9%

resultado_de_fp
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
normal
30 
sarcopenia
23 

Length

Max length10
Median length6
Mean length7.7358491
Min length6

Characters and Unicode

Total characters410
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rowsarcopenia

Common Values

ValueCountFrequency (%)
normal 30
56.6%
sarcopenia 23
43.4%

Length

2025-05-02T12:22:08.193219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:08.236972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 30
56.6%
sarcopenia 23
43.4%

Most occurring characters

ValueCountFrequency (%)
a 76
18.5%
n 53
12.9%
o 53
12.9%
r 53
12.9%
m 30
 
7.3%
l 30
 
7.3%
s 23
 
5.6%
c 23
 
5.6%
p 23
 
5.6%
e 23
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 76
18.5%
n 53
12.9%
o 53
12.9%
r 53
12.9%
m 30
 
7.3%
l 30
 
7.3%
s 23
 
5.6%
c 23
 
5.6%
p 23
 
5.6%
e 23
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 76
18.5%
n 53
12.9%
o 53
12.9%
r 53
12.9%
m 30
 
7.3%
l 30
 
7.3%
s 23
 
5.6%
c 23
 
5.6%
p 23
 
5.6%
e 23
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 76
18.5%
n 53
12.9%
o 53
12.9%
r 53
12.9%
m 30
 
7.3%
l 30
 
7.3%
s 23
 
5.6%
c 23
 
5.6%
p 23
 
5.6%
e 23
 
5.6%

prueba_de_la_silla
Real number (ℝ)

High correlation  Missing 

Distinct48
Distinct (%)92.3%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean16.679231
Minimum8.53
Maximum28.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:08.282595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.53
5-th percentile10.959
Q113.765
median15.5
Q318.5875
95-th percentile25.3235
Maximum28.07
Range19.54
Interquartile range (IQR)4.8225

Descriptive statistics

Standard deviation4.4258704
Coefficient of variation (CV)0.26535219
Kurtosis0.17572344
Mean16.679231
Median Absolute Deviation (MAD)2.745
Skewness0.70429746
Sum867.32
Variance19.588329
MonotonicityNot monotonic
2025-05-02T12:22:08.340218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
15.5 3
 
5.7%
18.3 2
 
3.8%
16.94 2
 
3.8%
21.31 1
 
1.9%
11.8 1
 
1.9%
16.69 1
 
1.9%
14.43 1
 
1.9%
15.22 1
 
1.9%
13.79 1
 
1.9%
28.07 1
 
1.9%
Other values (38) 38
71.7%
ValueCountFrequency (%)
8.53 1
1.9%
9.75 1
1.9%
10.53 1
1.9%
11.31 1
1.9%
11.47 1
1.9%
11.8 1
1.9%
12 1
1.9%
12.09 1
1.9%
12.66 1
1.9%
12.81 1
1.9%
ValueCountFrequency (%)
28.07 1
1.9%
26.19 1
1.9%
25.56 1
1.9%
25.13 1
1.9%
24.97 1
1.9%
24.32 1
1.9%
21.4 1
1.9%
21.31 1
1.9%
21.12 1
1.9%
19.84 1
1.9%

resultado_de_ps
Categorical

High correlation 

Distinct5
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
1.0
23 
2.0
18 
3.0
4.0
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters159
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 23
43.4%
2.0 18
34.0%
3.0 8
 
15.1%
4.0 3
 
5.7%
0.0 1
 
1.9%

Length

2025-05-02T12:22:08.393745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:08.435246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 23
43.4%
2.0 18
34.0%
3.0 8
 
15.1%
4.0 3
 
5.7%
0.0 1
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 54
34.0%
. 53
33.3%
1 23
14.5%
2 18
 
11.3%
3 8
 
5.0%
4 3
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 54
34.0%
. 53
33.3%
1 23
14.5%
2 18
 
11.3%
3 8
 
5.0%
4 3
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 54
34.0%
. 53
33.3%
1 23
14.5%
2 18
 
11.3%
3 8
 
5.0%
4 3
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 54
34.0%
. 53
33.3%
1 23
14.5%
2 18
 
11.3%
3 8
 
5.0%
4 3
 
1.9%

puntaje_de_balance
Categorical

High correlation 

Distinct4
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
4.0
26 
2.0
21 
3.0
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters159
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row2.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
4.0 26
49.1%
2.0 21
39.6%
3.0 4
 
7.5%
1.0 2
 
3.8%

Length

2025-05-02T12:22:08.482708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:08.523046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 26
49.1%
2.0 21
39.6%
3.0 4
 
7.5%
1.0 2
 
3.8%

Most occurring characters

ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
4 26
16.4%
2 21
 
13.2%
3 4
 
2.5%
1 2
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
4 26
16.4%
2 21
 
13.2%
3 4
 
2.5%
1 2
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
4 26
16.4%
2 21
 
13.2%
3 4
 
2.5%
1 2
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
4 26
16.4%
2 21
 
13.2%
3 4
 
2.5%
1 2
 
1.3%

velocidad_de_la_marcha
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80056604
Minimum0.2
Maximum1.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:08.569891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.358
Q10.7
median0.83
Q30.93
95-th percentile1.12
Maximum1.53
Range1.33
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.24245075
Coefficient of variation (CV)0.30284916
Kurtosis1.6349839
Mean0.80056604
Median Absolute Deviation (MAD)0.1
Skewness0.084030206
Sum42.43
Variance0.058782366
MonotonicityNot monotonic
2025-05-02T12:22:08.625631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.93 4
 
7.5%
0.75 3
 
5.7%
0.9 3
 
5.7%
0.94 2
 
3.8%
0.77 2
 
3.8%
0.76 2
 
3.8%
0.8 2
 
3.8%
0.85 2
 
3.8%
0.34 2
 
3.8%
0.78 2
 
3.8%
Other values (25) 29
54.7%
ValueCountFrequency (%)
0.2 1
1.9%
0.34 2
3.8%
0.37 1
1.9%
0.38 1
1.9%
0.46 1
1.9%
0.48 1
1.9%
0.57 1
1.9%
0.6 1
1.9%
0.62 1
1.9%
0.63 1
1.9%
ValueCountFrequency (%)
1.53 1
 
1.9%
1.41 1
 
1.9%
1.12 2
3.8%
1.09 1
 
1.9%
0.97 1
 
1.9%
0.96 1
 
1.9%
0.95 1
 
1.9%
0.94 2
3.8%
0.93 4
7.5%
0.92 1
 
1.9%

resultado_de_velocidad
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
normal
31 
sarcopenia
22 

Length

Max length10
Median length6
Mean length7.6603774
Min length6

Characters and Unicode

Total characters406
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rowsarcopenia

Common Values

ValueCountFrequency (%)
normal 31
58.5%
sarcopenia 22
41.5%

Length

2025-05-02T12:22:08.683019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:08.726116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 31
58.5%
sarcopenia 22
41.5%

Most occurring characters

ValueCountFrequency (%)
a 75
18.5%
n 53
13.1%
o 53
13.1%
r 53
13.1%
m 31
7.6%
l 31
7.6%
s 22
 
5.4%
c 22
 
5.4%
p 22
 
5.4%
e 22
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 75
18.5%
n 53
13.1%
o 53
13.1%
r 53
13.1%
m 31
7.6%
l 31
7.6%
s 22
 
5.4%
c 22
 
5.4%
p 22
 
5.4%
e 22
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 75
18.5%
n 53
13.1%
o 53
13.1%
r 53
13.1%
m 31
7.6%
l 31
7.6%
s 22
 
5.4%
c 22
 
5.4%
p 22
 
5.4%
e 22
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 75
18.5%
n 53
13.1%
o 53
13.1%
r 53
13.1%
m 31
7.6%
l 31
7.6%
s 22
 
5.4%
c 22
 
5.4%
p 22
 
5.4%
e 22
 
5.4%

puntaje_de_velocidad_de_marcha
Categorical

High correlation 

Distinct4
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
4.0
27 
3.0
13 
2.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters159
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
4.0 27
50.9%
3.0 13
24.5%
2.0 9
 
17.0%
1.0 4
 
7.5%

Length

2025-05-02T12:22:08.768068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:08.808488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 27
50.9%
3.0 13
24.5%
2.0 9
 
17.0%
1.0 4
 
7.5%

Most occurring characters

ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
4 27
17.0%
3 13
 
8.2%
2 9
 
5.7%
1 4
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
4 27
17.0%
3 13
 
8.2%
2 9
 
5.7%
1 4
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
4 27
17.0%
3 13
 
8.2%
2 9
 
5.7%
1 4
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
4 27
17.0%
3 13
 
8.2%
2 9
 
5.7%
1 4
 
2.5%

puntaje_sppb
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0188679
Minimum3
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:08.849475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q16
median8
Q310
95-th percentile11.4
Maximum12
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5380987
Coefficient of variation (CV)0.31651583
Kurtosis-1.1646074
Mean8.0188679
Median Absolute Deviation (MAD)2
Skewness-0.24196787
Sum425
Variance6.4419448
MonotonicityNot monotonic
2025-05-02T12:22:08.892075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 12
22.6%
11 6
11.3%
7 6
11.3%
6 6
11.3%
9 5
9.4%
5 5
9.4%
4 5
9.4%
8 4
 
7.5%
12 3
 
5.7%
3 1
 
1.9%
ValueCountFrequency (%)
3 1
 
1.9%
4 5
9.4%
5 5
9.4%
6 6
11.3%
7 6
11.3%
8 4
 
7.5%
9 5
9.4%
10 12
22.6%
11 6
11.3%
12 3
 
5.7%
ValueCountFrequency (%)
12 3
 
5.7%
11 6
11.3%
10 12
22.6%
9 5
9.4%
8 4
 
7.5%
7 6
11.3%
6 6
11.3%
5 5
9.4%
4 5
9.4%
3 1
 
1.9%

clasificacion_de_estado_fisico
Categorical

High correlation 

Distinct3
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
alto
21 
intermedio
18 
bajo
14 

Length

Max length10
Median length4
Mean length6.0377358
Min length4

Characters and Unicode

Total characters320
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowintermedio
2nd rowintermedio
3rd rowintermedio
4th rowalto
5th rowbajo

Common Values

ValueCountFrequency (%)
alto 21
39.6%
intermedio 18
34.0%
bajo 14
26.4%

Length

2025-05-02T12:22:08.940229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:08.982570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
alto 21
39.6%
intermedio 18
34.0%
bajo 14
26.4%

Most occurring characters

ValueCountFrequency (%)
o 53
16.6%
t 39
12.2%
i 36
11.2%
e 36
11.2%
a 35
10.9%
l 21
 
6.6%
n 18
 
5.6%
r 18
 
5.6%
m 18
 
5.6%
d 18
 
5.6%
Other values (2) 28
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 320
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 53
16.6%
t 39
12.2%
i 36
11.2%
e 36
11.2%
a 35
10.9%
l 21
 
6.6%
n 18
 
5.6%
r 18
 
5.6%
m 18
 
5.6%
d 18
 
5.6%
Other values (2) 28
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 320
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 53
16.6%
t 39
12.2%
i 36
11.2%
e 36
11.2%
a 35
10.9%
l 21
 
6.6%
n 18
 
5.6%
r 18
 
5.6%
m 18
 
5.6%
d 18
 
5.6%
Other values (2) 28
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 320
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 53
16.6%
t 39
12.2%
i 36
11.2%
e 36
11.2%
a 35
10.9%
l 21
 
6.6%
n 18
 
5.6%
r 18
 
5.6%
m 18
 
5.6%
d 18
 
5.6%
Other values (2) 28
8.8%

fuerza
Categorical

High correlation 

Distinct3
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
ninguna
32 
poca
16 
mucha

Length

Max length7
Median length7
Mean length5.9056604
Min length4

Characters and Unicode

Total characters313
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowninguna
2nd rowninguna
3rd rowninguna
4th rowninguna
5th rowpoca

Common Values

ValueCountFrequency (%)
ninguna 32
60.4%
poca 16
30.2%
mucha 5
 
9.4%

Length

2025-05-02T12:22:09.029921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:09.072145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ninguna 32
60.4%
poca 16
30.2%
mucha 5
 
9.4%

Most occurring characters

ValueCountFrequency (%)
n 96
30.7%
a 53
16.9%
u 37
 
11.8%
i 32
 
10.2%
g 32
 
10.2%
c 21
 
6.7%
p 16
 
5.1%
o 16
 
5.1%
m 5
 
1.6%
h 5
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 96
30.7%
a 53
16.9%
u 37
 
11.8%
i 32
 
10.2%
g 32
 
10.2%
c 21
 
6.7%
p 16
 
5.1%
o 16
 
5.1%
m 5
 
1.6%
h 5
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 96
30.7%
a 53
16.9%
u 37
 
11.8%
i 32
 
10.2%
g 32
 
10.2%
c 21
 
6.7%
p 16
 
5.1%
o 16
 
5.1%
m 5
 
1.6%
h 5
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 96
30.7%
a 53
16.9%
u 37
 
11.8%
i 32
 
10.2%
g 32
 
10.2%
c 21
 
6.7%
p 16
 
5.1%
o 16
 
5.1%
m 5
 
1.6%
h 5
 
1.6%

caminata
Categorical

High correlation 

Distinct3
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
ninguna
39 
poca
12 
mucha
 
2

Length

Max length7
Median length7
Mean length6.245283
Min length4

Characters and Unicode

Total characters331
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowninguna
2nd rowninguna
3rd rowninguna
4th rowninguna
5th rowpoca

Common Values

ValueCountFrequency (%)
ninguna 39
73.6%
poca 12
 
22.6%
mucha 2
 
3.8%

Length

2025-05-02T12:22:09.118137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:09.159146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ninguna 39
73.6%
poca 12
 
22.6%
mucha 2
 
3.8%

Most occurring characters

ValueCountFrequency (%)
n 117
35.3%
a 53
16.0%
u 41
 
12.4%
i 39
 
11.8%
g 39
 
11.8%
c 14
 
4.2%
p 12
 
3.6%
o 12
 
3.6%
m 2
 
0.6%
h 2
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 117
35.3%
a 53
16.0%
u 41
 
12.4%
i 39
 
11.8%
g 39
 
11.8%
c 14
 
4.2%
p 12
 
3.6%
o 12
 
3.6%
m 2
 
0.6%
h 2
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 117
35.3%
a 53
16.0%
u 41
 
12.4%
i 39
 
11.8%
g 39
 
11.8%
c 14
 
4.2%
p 12
 
3.6%
o 12
 
3.6%
m 2
 
0.6%
h 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 117
35.3%
a 53
16.0%
u 41
 
12.4%
i 39
 
11.8%
g 39
 
11.8%
c 14
 
4.2%
p 12
 
3.6%
o 12
 
3.6%
m 2
 
0.6%
h 2
 
0.6%

levantarse
Categorical

High correlation 

Distinct3
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
ninguna
35 
poca
16 
mucha
 
2

Length

Max length7
Median length7
Mean length6.0188679
Min length4

Characters and Unicode

Total characters319
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowninguna
2nd rowninguna
3rd rowninguna
4th rowninguna
5th rowpoca

Common Values

ValueCountFrequency (%)
ninguna 35
66.0%
poca 16
30.2%
mucha 2
 
3.8%

Length

2025-05-02T12:22:09.206097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:09.248491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ninguna 35
66.0%
poca 16
30.2%
mucha 2
 
3.8%

Most occurring characters

ValueCountFrequency (%)
n 105
32.9%
a 53
16.6%
u 37
 
11.6%
i 35
 
11.0%
g 35
 
11.0%
c 18
 
5.6%
p 16
 
5.0%
o 16
 
5.0%
m 2
 
0.6%
h 2
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 105
32.9%
a 53
16.6%
u 37
 
11.6%
i 35
 
11.0%
g 35
 
11.0%
c 18
 
5.6%
p 16
 
5.0%
o 16
 
5.0%
m 2
 
0.6%
h 2
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 105
32.9%
a 53
16.6%
u 37
 
11.6%
i 35
 
11.0%
g 35
 
11.0%
c 18
 
5.6%
p 16
 
5.0%
o 16
 
5.0%
m 2
 
0.6%
h 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 105
32.9%
a 53
16.6%
u 37
 
11.6%
i 35
 
11.0%
g 35
 
11.0%
c 18
 
5.6%
p 16
 
5.0%
o 16
 
5.0%
m 2
 
0.6%
h 2
 
0.6%

subir_escaleras
Categorical

High correlation 

Distinct3
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
ninguna
28 
poca
21 
mucha

Length

Max length7
Median length7
Mean length5.6603774
Min length4

Characters and Unicode

Total characters300
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpoca
2nd rowmucha
3rd rowninguna
4th rowninguna
5th rowpoca

Common Values

ValueCountFrequency (%)
ninguna 28
52.8%
poca 21
39.6%
mucha 4
 
7.5%

Length

2025-05-02T12:22:09.294192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:09.335218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ninguna 28
52.8%
poca 21
39.6%
mucha 4
 
7.5%

Most occurring characters

ValueCountFrequency (%)
n 84
28.0%
a 53
17.7%
u 32
 
10.7%
i 28
 
9.3%
g 28
 
9.3%
c 25
 
8.3%
p 21
 
7.0%
o 21
 
7.0%
m 4
 
1.3%
h 4
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 84
28.0%
a 53
17.7%
u 32
 
10.7%
i 28
 
9.3%
g 28
 
9.3%
c 25
 
8.3%
p 21
 
7.0%
o 21
 
7.0%
m 4
 
1.3%
h 4
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 84
28.0%
a 53
17.7%
u 32
 
10.7%
i 28
 
9.3%
g 28
 
9.3%
c 25
 
8.3%
p 21
 
7.0%
o 21
 
7.0%
m 4
 
1.3%
h 4
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 84
28.0%
a 53
17.7%
u 32
 
10.7%
i 28
 
9.3%
g 28
 
9.3%
c 25
 
8.3%
p 21
 
7.0%
o 21
 
7.0%
m 4
 
1.3%
h 4
 
1.3%

caidas
Categorical

High correlation 

Distinct3
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
ninguna
29 
1 - 3 caidas
19 
4 o mas caidas

Length

Max length14
Median length7
Mean length9.4528302
Min length7

Characters and Unicode

Total characters501
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowninguna
2nd rowninguna
3rd rowninguna
4th rowninguna
5th rowninguna

Common Values

ValueCountFrequency (%)
ninguna 29
54.7%
1 - 3 caidas 19
35.8%
4 o mas caidas 5
 
9.4%

Length

2025-05-02T12:22:09.383271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:09.426332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ninguna 29
23.2%
caidas 24
19.2%
1 19
15.2%
19
15.2%
3 19
15.2%
4 5
 
4.0%
o 5
 
4.0%
mas 5
 
4.0%

Most occurring characters

ValueCountFrequency (%)
n 87
17.4%
a 82
16.4%
72
14.4%
i 53
10.6%
g 29
 
5.8%
u 29
 
5.8%
s 29
 
5.8%
c 24
 
4.8%
d 24
 
4.8%
1 19
 
3.8%
Other values (5) 53
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 501
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 87
17.4%
a 82
16.4%
72
14.4%
i 53
10.6%
g 29
 
5.8%
u 29
 
5.8%
s 29
 
5.8%
c 24
 
4.8%
d 24
 
4.8%
1 19
 
3.8%
Other values (5) 53
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 501
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 87
17.4%
a 82
16.4%
72
14.4%
i 53
10.6%
g 29
 
5.8%
u 29
 
5.8%
s 29
 
5.8%
c 24
 
4.8%
d 24
 
4.8%
1 19
 
3.8%
Other values (5) 53
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 501
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 87
17.4%
a 82
16.4%
72
14.4%
i 53
10.6%
g 29
 
5.8%
u 29
 
5.8%
s 29
 
5.8%
c 24
 
4.8%
d 24
 
4.8%
1 19
 
3.8%
Other values (5) 53
10.6%

sarc_f_puntaje
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.245283
Minimum0
Maximum7
Zeros13
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size556.0 B
2025-05-02T12:22:09.465221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5.4
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9797597
Coefficient of variation (CV)0.88174172
Kurtosis-0.92403577
Mean2.245283
Median Absolute Deviation (MAD)2
Skewness0.47967385
Sum119
Variance3.9194485
MonotonicityNot monotonic
2025-05-02T12:22:09.507879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 13
24.5%
0 13
24.5%
4 11
20.8%
3 5
 
9.4%
2 4
 
7.5%
5 4
 
7.5%
6 2
 
3.8%
7 1
 
1.9%
ValueCountFrequency (%)
0 13
24.5%
1 13
24.5%
2 4
 
7.5%
3 5
 
9.4%
4 11
20.8%
5 4
 
7.5%
6 2
 
3.8%
7 1
 
1.9%
ValueCountFrequency (%)
7 1
 
1.9%
6 2
 
3.8%
5 4
 
7.5%
4 11
20.8%
3 5
 
9.4%
2 4
 
7.5%
1 13
24.5%
0 13
24.5%

sarc_f_resultado
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
baja probabilidad de sarcopenia
35 
alta probabilidad de sarcopenia
18 

Length

Max length31
Median length31
Mean length31
Min length31

Characters and Unicode

Total characters1643
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbaja probabilidad de sarcopenia
2nd rowbaja probabilidad de sarcopenia
3rd rowbaja probabilidad de sarcopenia
4th rowbaja probabilidad de sarcopenia
5th rowalta probabilidad de sarcopenia

Common Values

ValueCountFrequency (%)
baja probabilidad de sarcopenia 35
66.0%
alta probabilidad de sarcopenia 18
34.0%

Length

2025-05-02T12:22:09.555520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:09.593879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
probabilidad 53
25.0%
de 53
25.0%
sarcopenia 53
25.0%
baja 35
16.5%
alta 18
 
8.5%

Most occurring characters

ValueCountFrequency (%)
a 318
19.4%
159
9.7%
i 159
9.7%
d 159
9.7%
b 141
8.6%
p 106
 
6.5%
r 106
 
6.5%
o 106
 
6.5%
e 106
 
6.5%
l 71
 
4.3%
Other values (5) 212
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 318
19.4%
159
9.7%
i 159
9.7%
d 159
9.7%
b 141
8.6%
p 106
 
6.5%
r 106
 
6.5%
o 106
 
6.5%
e 106
 
6.5%
l 71
 
4.3%
Other values (5) 212
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 318
19.4%
159
9.7%
i 159
9.7%
d 159
9.7%
b 141
8.6%
p 106
 
6.5%
r 106
 
6.5%
o 106
 
6.5%
e 106
 
6.5%
l 71
 
4.3%
Other values (5) 212
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 318
19.4%
159
9.7%
i 159
9.7%
d 159
9.7%
b 141
8.6%
p 106
 
6.5%
r 106
 
6.5%
o 106
 
6.5%
e 106
 
6.5%
l 71
 
4.3%
Other values (5) 212
12.9%

capacidad_de_usar_el_telefono
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
49 
no
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowno

Common Values

ValueCountFrequency (%)
si 49
92.5%
no 4
 
7.5%

Length

2025-05-02T12:22:09.637054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:09.674192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 49
92.5%
no 4
 
7.5%

Most occurring characters

ValueCountFrequency (%)
s 49
46.2%
i 49
46.2%
n 4
 
3.8%
o 4
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 49
46.2%
i 49
46.2%
n 4
 
3.8%
o 4
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 49
46.2%
i 49
46.2%
n 4
 
3.8%
o 4
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 49
46.2%
i 49
46.2%
n 4
 
3.8%
o 4
 
3.8%

transporte
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
47 
no

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowno
3rd rowsi
4th rowsi
5th rowno

Common Values

ValueCountFrequency (%)
si 47
88.7%
no 6
 
11.3%

Length

2025-05-02T12:22:09.713556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:09.751480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 47
88.7%
no 6
 
11.3%

Most occurring characters

ValueCountFrequency (%)
s 47
44.3%
i 47
44.3%
n 6
 
5.7%
o 6
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 47
44.3%
i 47
44.3%
n 6
 
5.7%
o 6
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 47
44.3%
i 47
44.3%
n 6
 
5.7%
o 6
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 47
44.3%
i 47
44.3%
n 6
 
5.7%
o 6
 
5.7%

medicacion
Categorical

Constant 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
53 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowsi

Common Values

ValueCountFrequency (%)
si 53
100.0%

Length

2025-05-02T12:22:09.792334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:09.827914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 53
100.0%

Most occurring characters

ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

finanzas
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
51 
no
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowsi
3rd rowsi
4th rowsi
5th rowsi

Common Values

ValueCountFrequency (%)
si 51
96.2%
no 2
 
3.8%

Length

2025-05-02T12:22:09.866233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:09.904094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 51
96.2%
no 2
 
3.8%

Most occurring characters

ValueCountFrequency (%)
s 51
48.1%
i 51
48.1%
n 2
 
1.9%
o 2
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 51
48.1%
i 51
48.1%
n 2
 
1.9%
o 2
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 51
48.1%
i 51
48.1%
n 2
 
1.9%
o 2
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 51
48.1%
i 51
48.1%
n 2
 
1.9%
o 2
 
1.9%

compras
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
41 
no
12 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowno
3rd rowsi
4th rowsi
5th rowno

Common Values

ValueCountFrequency (%)
si 41
77.4%
no 12
 
22.6%

Length

2025-05-02T12:22:09.944726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:09.982620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 41
77.4%
no 12
 
22.6%

Most occurring characters

ValueCountFrequency (%)
s 41
38.7%
i 41
38.7%
n 12
 
11.3%
o 12
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 41
38.7%
i 41
38.7%
n 12
 
11.3%
o 12
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 41
38.7%
i 41
38.7%
n 12
 
11.3%
o 12
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 41
38.7%
i 41
38.7%
n 12
 
11.3%
o 12
 
11.3%

cocina
Categorical

Constant 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
53 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowsi

Common Values

ValueCountFrequency (%)
si 53
100.0%

Length

2025-05-02T12:22:10.024001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:10.060425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 53
100.0%

Most occurring characters

ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

cuidado_del_hogar
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
50 
no
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowno

Common Values

ValueCountFrequency (%)
si 50
94.3%
no 3
 
5.7%

Length

2025-05-02T12:22:10.105602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:10.152141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 50
94.3%
no 3
 
5.7%

Most occurring characters

ValueCountFrequency (%)
s 50
47.2%
i 50
47.2%
n 3
 
2.8%
o 3
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 50
47.2%
i 50
47.2%
n 3
 
2.8%
o 3
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 50
47.2%
i 50
47.2%
n 3
 
2.8%
o 3
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 50
47.2%
i 50
47.2%
n 3
 
2.8%
o 3
 
2.8%

lavanderia
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
52 
no
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowsi

Common Values

ValueCountFrequency (%)
si 52
98.1%
no 1
 
1.9%

Length

2025-05-02T12:22:10.203134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:10.249276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 52
98.1%
no 1
 
1.9%

Most occurring characters

ValueCountFrequency (%)
s 52
49.1%
i 52
49.1%
n 1
 
0.9%
o 1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 52
49.1%
i 52
49.1%
n 1
 
0.9%
o 1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 52
49.1%
i 52
49.1%
n 1
 
0.9%
o 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 52
49.1%
i 52
49.1%
n 1
 
0.9%
o 1
 
0.9%

resultado_de_lawton
Categorical

High correlation 

Distinct5
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
8.0
37 
7.0
6.0
4.0
 
1
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters159
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)3.8%

Sample

1st row7.0
2nd row6.0
3rd row8.0
4th row8.0
5th row4.0

Common Values

ValueCountFrequency (%)
8.0 37
69.8%
7.0 7
 
13.2%
6.0 7
 
13.2%
4.0 1
 
1.9%
5.0 1
 
1.9%

Length

2025-05-02T12:22:10.295795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:10.344332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
8.0 37
69.8%
7.0 7
 
13.2%
6.0 7
 
13.2%
4.0 1
 
1.9%
5.0 1
 
1.9%

Most occurring characters

ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
8 37
23.3%
7 7
 
4.4%
6 7
 
4.4%
4 1
 
0.6%
5 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
8 37
23.3%
7 7
 
4.4%
6 7
 
4.4%
4 1
 
0.6%
5 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
8 37
23.3%
7 7
 
4.4%
6 7
 
4.4%
4 1
 
0.6%
5 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
8 37
23.3%
7 7
 
4.4%
6 7
 
4.4%
4 1
 
0.6%
5 1
 
0.6%

interpretacion_lawton
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
independencia total
36 
deteriro funcional
17 

Length

Max length19
Median length19
Mean length18.679245
Min length18

Characters and Unicode

Total characters990
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdeteriro funcional
2nd rowdeteriro funcional
3rd rowindependencia total
4th rowindependencia total
5th rowdeteriro funcional

Common Values

ValueCountFrequency (%)
independencia total 36
67.9%
deteriro funcional 17
32.1%

Length

2025-05-02T12:22:10.403293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:10.448150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
independencia 36
34.0%
total 36
34.0%
deteriro 17
16.0%
funcional 17
16.0%

Most occurring characters

ValueCountFrequency (%)
n 142
14.3%
e 142
14.3%
i 106
10.7%
d 89
9.0%
a 89
9.0%
t 89
9.0%
o 70
7.1%
c 53
 
5.4%
53
 
5.4%
l 53
 
5.4%
Other values (4) 104
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 142
14.3%
e 142
14.3%
i 106
10.7%
d 89
9.0%
a 89
9.0%
t 89
9.0%
o 70
7.1%
c 53
 
5.4%
53
 
5.4%
l 53
 
5.4%
Other values (4) 104
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 142
14.3%
e 142
14.3%
i 106
10.7%
d 89
9.0%
a 89
9.0%
t 89
9.0%
o 70
7.1%
c 53
 
5.4%
53
 
5.4%
l 53
 
5.4%
Other values (4) 104
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 142
14.3%
e 142
14.3%
i 106
10.7%
d 89
9.0%
a 89
9.0%
t 89
9.0%
o 70
7.1%
c 53
 
5.4%
53
 
5.4%
l 53
 
5.4%
Other values (4) 104
10.5%

baño
Categorical

Constant 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
53 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowsi

Common Values

ValueCountFrequency (%)
si 53
100.0%

Length

2025-05-02T12:22:10.491328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:10.527843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 53
100.0%

Most occurring characters

ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

vestido
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
52 
no
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowsi

Common Values

ValueCountFrequency (%)
si 52
98.1%
no 1
 
1.9%

Length

2025-05-02T12:22:10.567826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:10.606032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 52
98.1%
no 1
 
1.9%

Most occurring characters

ValueCountFrequency (%)
s 52
49.1%
i 52
49.1%
n 1
 
0.9%
o 1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 52
49.1%
i 52
49.1%
n 1
 
0.9%
o 1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 52
49.1%
i 52
49.1%
n 1
 
0.9%
o 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 52
49.1%
i 52
49.1%
n 1
 
0.9%
o 1
 
0.9%

sanitario
Categorical

Constant 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
53 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowsi

Common Values

ValueCountFrequency (%)
si 53
100.0%

Length

2025-05-02T12:22:10.646094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:10.684034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 53
100.0%

Most occurring characters

ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

transferencia
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
47 
no

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowno
3rd rowsi
4th rowsi
5th rowno

Common Values

ValueCountFrequency (%)
si 47
88.7%
no 6
 
11.3%

Length

2025-05-02T12:22:10.723616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:10.761130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 47
88.7%
no 6
 
11.3%

Most occurring characters

ValueCountFrequency (%)
s 47
44.3%
i 47
44.3%
n 6
 
5.7%
o 6
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 47
44.3%
i 47
44.3%
n 6
 
5.7%
o 6
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 47
44.3%
i 47
44.3%
n 6
 
5.7%
o 6
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 47
44.3%
i 47
44.3%
n 6
 
5.7%
o 6
 
5.7%

continencia
Categorical

Constant 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
53 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowsi

Common Values

ValueCountFrequency (%)
si 53
100.0%

Length

2025-05-02T12:22:10.802284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:10.838185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 53
100.0%

Most occurring characters

ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

alimentacion
Categorical

Constant 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
53 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowsi

Common Values

ValueCountFrequency (%)
si 53
100.0%

Length

2025-05-02T12:22:10.876922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:10.912846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 53
100.0%

Most occurring characters

ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 53
50.0%
i 53
50.0%

puntaje_de_katz
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
6.0
47 
5.0
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters159
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st row6.0
2nd row5.0
3rd row6.0
4th row6.0
5th row5.0

Common Values

ValueCountFrequency (%)
6.0 47
88.7%
5.0 5
 
9.4%
4.0 1
 
1.9%

Length

2025-05-02T12:22:10.951228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:10.990604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
6.0 47
88.7%
5.0 5
 
9.4%
4.0 1
 
1.9%

Most occurring characters

ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
6 47
29.6%
5 5
 
3.1%
4 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
6 47
29.6%
5 5
 
3.1%
4 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
6 47
29.6%
5 5
 
3.1%
4 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
6 47
29.6%
5 5
 
3.1%
4 1
 
0.6%

resultado_de_katz
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
independencia total
47 
deterioro funcional

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1007
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowindependencia total
2nd rowdeterioro funcional
3rd rowindependencia total
4th rowindependencia total
5th rowdeterioro funcional

Common Values

ValueCountFrequency (%)
independencia total 47
88.7%
deterioro funcional 6
 
11.3%

Length

2025-05-02T12:22:11.036319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:11.077512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
independencia 47
44.3%
total 47
44.3%
deterioro 6
 
5.7%
funcional 6
 
5.7%

Most occurring characters

ValueCountFrequency (%)
n 153
15.2%
e 153
15.2%
i 106
10.5%
d 100
9.9%
a 100
9.9%
t 100
9.9%
o 65
6.5%
c 53
 
5.3%
53
 
5.3%
l 53
 
5.3%
Other values (4) 71
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 153
15.2%
e 153
15.2%
i 106
10.5%
d 100
9.9%
a 100
9.9%
t 100
9.9%
o 65
6.5%
c 53
 
5.3%
53
 
5.3%
l 53
 
5.3%
Other values (4) 71
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 153
15.2%
e 153
15.2%
i 106
10.5%
d 100
9.9%
a 100
9.9%
t 100
9.9%
o 65
6.5%
c 53
 
5.3%
53
 
5.3%
l 53
 
5.3%
Other values (4) 71
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 153
15.2%
e 153
15.2%
i 106
10.5%
d 100
9.9%
a 100
9.9%
t 100
9.9%
o 65
6.5%
c 53
 
5.3%
53
 
5.3%
l 53
 
5.3%
Other values (4) 71
7.1%

ejercicio
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
no
28 
si
25 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowno

Common Values

ValueCountFrequency (%)
no 28
52.8%
si 25
47.2%

Length

2025-05-02T12:22:11.120972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:11.159526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 28
52.8%
si 25
47.2%

Most occurring characters

ValueCountFrequency (%)
n 28
26.4%
o 28
26.4%
s 25
23.6%
i 25
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 28
26.4%
o 28
26.4%
s 25
23.6%
i 25
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 28
26.4%
o 28
26.4%
s 25
23.6%
i 25
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 28
26.4%
o 28
26.4%
s 25
23.6%
i 25
23.6%

tiempo_de_ejercicio
Categorical

High correlation 

Distinct5
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
no
29 
121 - 180 mins
mas de 180 mins
30 - 60 mins
61 - 120 mins

Length

Max length15
Median length2
Mean length7.3773585
Min length2

Characters and Unicode

Total characters391
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30 - 60 mins
2nd row30 - 60 mins
3rd row121 - 180 mins
4th row121 - 180 mins
5th rowno

Common Values

ValueCountFrequency (%)
no 29
54.7%
121 - 180 mins 9
 
17.0%
mas de 180 mins 8
 
15.1%
30 - 60 mins 4
 
7.5%
61 - 120 mins 3
 
5.7%

Length

2025-05-02T12:22:11.205454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:11.250994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 29
23.2%
mins 24
19.2%
180 17
13.6%
16
12.8%
121 9
 
7.2%
mas 8
 
6.4%
de 8
 
6.4%
30 4
 
3.2%
60 4
 
3.2%
61 3
 
2.4%

Most occurring characters

ValueCountFrequency (%)
72
18.4%
n 53
13.6%
1 41
10.5%
m 32
8.2%
s 32
8.2%
o 29
7.4%
0 28
 
7.2%
i 24
 
6.1%
8 17
 
4.3%
- 16
 
4.1%
Other values (6) 47
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 391
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
72
18.4%
n 53
13.6%
1 41
10.5%
m 32
8.2%
s 32
8.2%
o 29
7.4%
0 28
 
7.2%
i 24
 
6.1%
8 17
 
4.3%
- 16
 
4.1%
Other values (6) 47
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 391
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
72
18.4%
n 53
13.6%
1 41
10.5%
m 32
8.2%
s 32
8.2%
o 29
7.4%
0 28
 
7.2%
i 24
 
6.1%
8 17
 
4.3%
- 16
 
4.1%
Other values (6) 47
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 391
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
72
18.4%
n 53
13.6%
1 41
10.5%
m 32
8.2%
s 32
8.2%
o 29
7.4%
0 28
 
7.2%
i 24
 
6.1%
8 17
 
4.3%
- 16
 
4.1%
Other values (6) 47
12.0%

medicamentos
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
39 
no
14 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowsi

Common Values

ValueCountFrequency (%)
si 39
73.6%
no 14
 
26.4%

Length

2025-05-02T12:22:11.300973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:11.341320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 39
73.6%
no 14
 
26.4%

Most occurring characters

ValueCountFrequency (%)
s 39
36.8%
i 39
36.8%
n 14
 
13.2%
o 14
 
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 39
36.8%
i 39
36.8%
n 14
 
13.2%
o 14
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 39
36.8%
i 39
36.8%
n 14
 
13.2%
o 14
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 39
36.8%
i 39
36.8%
n 14
 
13.2%
o 14
 
13.2%

medicamento_1
Categorical

High correlation  Missing 

Distinct4
Distinct (%)10.3%
Missing14
Missing (%)26.4%
Memory size3.4 KiB
antihipertensivos
22 
antidiabeticos
13 
hipnoticos o ansioliticos
antidepresivos
 
1

Length

Max length25
Median length17
Mean length16.538462
Min length14

Characters and Unicode

Total characters645
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.6%

Sample

1st rowantidiabeticos
2nd rowantidiabeticos
3rd rowantihipertensivos
4th rowantidiabeticos
5th rowantihipertensivos

Common Values

ValueCountFrequency (%)
antihipertensivos 22
41.5%
antidiabeticos 13
24.5%
hipnoticos o ansioliticos 3
 
5.7%
antidepresivos 1
 
1.9%
(Missing) 14
26.4%

Length

2025-05-02T12:22:11.386851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:11.432165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
antihipertensivos 22
48.9%
antidiabeticos 13
28.9%
hipnoticos 3
 
6.7%
o 3
 
6.7%
ansioliticos 3
 
6.7%
antidepresivos 1
 
2.2%

Most occurring characters

ValueCountFrequency (%)
i 122
18.9%
t 77
11.9%
s 68
10.5%
n 64
9.9%
e 59
9.1%
a 52
8.1%
o 51
7.9%
p 26
 
4.0%
h 25
 
3.9%
r 23
 
3.6%
Other values (6) 78
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 122
18.9%
t 77
11.9%
s 68
10.5%
n 64
9.9%
e 59
9.1%
a 52
8.1%
o 51
7.9%
p 26
 
4.0%
h 25
 
3.9%
r 23
 
3.6%
Other values (6) 78
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 122
18.9%
t 77
11.9%
s 68
10.5%
n 64
9.9%
e 59
9.1%
a 52
8.1%
o 51
7.9%
p 26
 
4.0%
h 25
 
3.9%
r 23
 
3.6%
Other values (6) 78
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 122
18.9%
t 77
11.9%
s 68
10.5%
n 64
9.9%
e 59
9.1%
a 52
8.1%
o 51
7.9%
p 26
 
4.0%
h 25
 
3.9%
r 23
 
3.6%
Other values (6) 78
12.1%

medicamento_2
Categorical

High correlation  Missing 

Distinct3
Distinct (%)33.3%
Missing44
Missing (%)83.0%
Memory size3.1 KiB
antidiabeticos
hipnoticos o ansioliticos
antidepresivos

Length

Max length25
Median length14
Mean length16.444444
Min length14

Characters and Unicode

Total characters148
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st rowantidiabeticos
2nd rowhipnoticos o ansioliticos
3rd rowantidiabeticos
4th rowhipnoticos o ansioliticos
5th rowantidiabeticos

Common Values

ValueCountFrequency (%)
antidiabeticos 6
 
11.3%
hipnoticos o ansioliticos 2
 
3.8%
antidepresivos 1
 
1.9%
(Missing) 44
83.0%

Length

2025-05-02T12:22:11.482974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:11.528396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
antidiabeticos 6
46.2%
hipnoticos 2
 
15.4%
o 2
 
15.4%
ansioliticos 2
 
15.4%
antidepresivos 1
 
7.7%

Most occurring characters

ValueCountFrequency (%)
i 30
20.3%
t 17
11.5%
o 17
11.5%
a 15
10.1%
s 14
9.5%
n 11
 
7.4%
c 10
 
6.8%
e 8
 
5.4%
d 7
 
4.7%
b 6
 
4.1%
Other values (6) 13
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 30
20.3%
t 17
11.5%
o 17
11.5%
a 15
10.1%
s 14
9.5%
n 11
 
7.4%
c 10
 
6.8%
e 8
 
5.4%
d 7
 
4.7%
b 6
 
4.1%
Other values (6) 13
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 30
20.3%
t 17
11.5%
o 17
11.5%
a 15
10.1%
s 14
9.5%
n 11
 
7.4%
c 10
 
6.8%
e 8
 
5.4%
d 7
 
4.7%
b 6
 
4.1%
Other values (6) 13
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 30
20.3%
t 17
11.5%
o 17
11.5%
a 15
10.1%
s 14
9.5%
n 11
 
7.4%
c 10
 
6.8%
e 8
 
5.4%
d 7
 
4.7%
b 6
 
4.1%
Other values (6) 13
8.8%

sarcopenia
Categorical

High correlation 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
si
27 
no
26 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowsi

Common Values

ValueCountFrequency (%)
si 27
50.9%
no 26
49.1%

Length

2025-05-02T12:22:11.576603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:11.614464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 27
50.9%
no 26
49.1%

Most occurring characters

ValueCountFrequency (%)
s 27
25.5%
i 27
25.5%
n 26
24.5%
o 26
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 27
25.5%
i 27
25.5%
n 26
24.5%
o 26
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 27
25.5%
i 27
25.5%
n 26
24.5%
o 26
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 27
25.5%
i 27
25.5%
n 26
24.5%
o 26
24.5%

nivel_de_sarcopenia
Categorical

High correlation 

Distinct4
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
sin sarcopenia
27 
sarcopenia moderada
13 
sarcopenia leve
sarcopenia severa

Length

Max length19
Median length14
Mean length15.622642
Min length14

Characters and Unicode

Total characters828
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsin sarcopenia
2nd rowsin sarcopenia
3rd rowsin sarcopenia
4th rowsin sarcopenia
5th rowsarcopenia moderada

Common Values

ValueCountFrequency (%)
sin sarcopenia 27
50.9%
sarcopenia moderada 13
24.5%
sarcopenia leve 9
 
17.0%
sarcopenia severa 4
 
7.5%

Length

2025-05-02T12:22:11.658429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T12:22:11.703229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sarcopenia 53
50.0%
sin 27
25.5%
moderada 13
 
12.3%
leve 9
 
8.5%
severa 4
 
3.8%

Most occurring characters

ValueCountFrequency (%)
a 136
16.4%
e 92
11.1%
s 84
10.1%
i 80
9.7%
n 80
9.7%
r 70
8.5%
o 66
8.0%
53
 
6.4%
c 53
 
6.4%
p 53
 
6.4%
Other values (4) 61
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 136
16.4%
e 92
11.1%
s 84
10.1%
i 80
9.7%
n 80
9.7%
r 70
8.5%
o 66
8.0%
53
 
6.4%
c 53
 
6.4%
p 53
 
6.4%
Other values (4) 61
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 136
16.4%
e 92
11.1%
s 84
10.1%
i 80
9.7%
n 80
9.7%
r 70
8.5%
o 66
8.0%
53
 
6.4%
c 53
 
6.4%
p 53
 
6.4%
Other values (4) 61
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 136
16.4%
e 92
11.1%
s 84
10.1%
i 80
9.7%
n 80
9.7%
r 70
8.5%
o 66
8.0%
53
 
6.4%
c 53
 
6.4%
p 53
 
6.4%
Other values (4) 61
7.4%

Interactions

2025-05-02T12:22:04.040786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.509613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.181247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.433599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.106086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.876366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.524181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.334178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.011318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.668419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.431698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.064384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.708926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.359947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.240318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.042780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.774337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.496103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.391255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.076247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.543784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.213927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.468203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.140277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.908992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.559935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.368357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.043896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.701625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.463594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.097447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.741712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.396528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.275161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.080418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.809329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.535332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.423547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.111002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.577803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.247107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.502475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.174226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.941241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.596646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.402424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.077052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.734316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.495227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.130038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.774219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.433267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.310710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.117424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.845001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.572113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.456903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.147795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.613639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.282332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.537773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.209893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.975907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.634363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.438023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.112016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.769528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.529385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.164304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.809030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.471703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.347458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.156060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.882610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.610987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.491046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.184591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.650515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.318659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.573010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.245221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.010560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.673414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.474329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.147552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.804301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.563237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.199081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.843409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.510010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.422107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.195223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.920858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.650380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.526270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.219618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.685687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.351275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.607611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.280203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.042241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.709856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.507142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.179863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.836890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.595037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.230973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.875796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.547118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.483533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.231219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.954775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.687469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.559031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.258670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.724014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.390711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.646063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.318978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.079200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.749643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.546208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.217910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.874305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.631293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.268722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.912582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.588630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.533533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.272504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.995391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.727861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.596262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.295529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.759068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.425376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.682062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.354921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.113046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.787486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.580541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.252378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.908945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.665203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.302455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.946245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.627310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.572037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.310836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.032421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.764912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.630225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.330597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.792454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.460194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.716137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.389512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.146134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.824445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.614699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.286181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.941740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.697173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.335498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.978798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.664265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.610024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.350625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.068649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.802230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.663014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.366319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.825136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.492800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.749504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.425343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.177153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.859794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.647439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.318307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.972712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.728528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.367085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.009578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.835296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.646210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.387800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.103492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.836439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.694806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.399032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.856592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.524497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.782197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.459412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.208152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.894831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.688218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.349665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.004015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.757878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.397912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.040435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.871242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.681168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.427630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.137955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.870736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.725041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.441805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.888449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.556911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.815542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.492623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.239317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.930676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.720607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.381580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.035375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.788662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.428135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.071044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.907065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.724030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.463625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.172156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.906030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.757641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.475433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.920185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.589106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.847633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.526343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.275530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.967014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.753397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.414442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.067636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.818780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.458406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.101961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.942391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.765005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.499150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.206603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.940339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.788647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.515098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.958041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.627491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.886737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.573681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.313116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.008264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.791915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.452747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.105471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.855148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.496112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.139084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.982816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.808087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.540396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.246316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.149251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.827007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.553862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:50.994207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.665884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.922983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.687899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.348403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.047275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.828325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.488284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.142547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.889450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.530840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.173527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.033748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.846801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.579647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.284721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.195289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.861789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.599020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.033034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.705440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.962893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.728033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.386502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.088925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.867658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.527506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.181401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.926627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.569007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.211919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.084848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.889879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.620430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.325153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.239568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.900493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.645217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.070959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.743214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.000483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.767228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.422406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.129358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.906069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.564883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.218195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.962653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.605172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.256198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.124765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.932162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.660415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.363585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.280493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.939854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.688252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.106629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.779496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.036932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.805211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.457432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.168329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.941972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.600665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.363653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.999044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.643177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.293137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.164656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.970687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.699459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.401949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.318518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.975257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.731566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:51.143966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:52.398083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.069498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:53.838811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:54.488596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.202935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:55.974767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:56.632400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:57.395298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.030016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:58.674060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:21:59.324233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:00.200555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.003972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:01.734952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:02.448752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:03.352838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-02T12:22:04.005618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-05-02T12:22:11.772653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
%_de_grasa%_de_musculoaltura_correctaaltura_en_cmaltura_mencionadaartritis_reumatoidecadera_totalcaidascaminatacapacidad_de_usar_el_telefonocircunferencia_de_la_pantorrillaclas_imcclasificacion_de_densidad_mineralclasificacion_de_estado_fisicocolumnacomprascp_sarcopeniacuidado_del_hogaredadejercicioestratificacion_de_riesgofinanzasfracturas_previasfuerzafuerza_de_prensionglucocorticoidesgrasa_resultadoimcimmeimme_resultadointerpretacion_lawtonlavanderialevantarsemasa_muscular_absolutamedicamento_1medicamento_2medicamentosnivel_de_sarcopeniapadres_con_fractura_de_caderapesoprobabilida_de_fractura_de_caderaprobabilida_de_fractura_por_fragilidadprueba_de_la_sillapuntaje_de_balancepuntaje_de_katzpuntaje_de_velocidad_de_marchapuntaje_sppbresultado_de_fpresultado_de_katzresultado_de_lawtonresultado_de_psresultado_de_velocidadsarc_f_puntajesarc_f_resultadosarcopeniasubir_escalerastabaquismotiempo_de_ejerciciotransferenciatransportevelocidad_de_la_marchavestido
%_de_grasa1.000-0.7990.000-0.276-0.2870.0000.1230.2560.4270.3110.3840.6220.2670.2680.2250.1130.4400.000-0.0710.2590.3060.0000.0000.237-0.2310.0000.8840.7950.3840.4060.0200.0000.3620.1100.3120.0000.0000.1700.0000.394-0.322-0.2710.1280.5920.3580.320-0.1960.5110.2930.1710.3170.4630.2980.5480.1230.0000.5290.1800.2930.293-0.3360.388
%_de_musculo-0.7991.0000.0000.4870.4700.0970.0660.3260.3350.203-0.0570.4470.0000.110-0.1320.0000.0000.077-0.1040.4870.0000.0000.3160.0000.3970.0000.586-0.569-0.0150.2050.0000.0000.3600.2460.2470.0000.1910.2270.000-0.0420.1090.097-0.2610.4360.3100.2620.3430.5220.1800.2130.2640.515-0.3850.4450.3230.0000.3920.2110.1800.1800.4480.388
altura_correcta0.0000.0001.0000.2090.2180.0000.0000.2160.1610.0000.2480.1160.0000.0000.0000.0000.0000.0000.1630.0000.0000.0000.0000.0000.1460.0000.0000.4910.0000.1840.0000.0000.0000.0000.3341.0000.0000.1650.0000.0000.0000.1860.2300.0000.0000.0790.0050.0000.0000.0000.0000.1380.0000.0000.0000.0000.0000.0000.0000.0000.1120.000
altura_en_cm-0.2760.4870.2091.0000.8780.0000.1780.3660.0000.0000.3440.1630.0000.2650.1650.0000.2640.000-0.2120.4740.0800.0000.0000.2830.4340.0000.434-0.1110.2450.1540.0000.0000.0000.6880.5540.0000.0980.2910.3220.424-0.162-0.166-0.1780.0000.0000.0000.1830.3650.0000.0000.0000.260-0.3060.2250.4400.1920.4380.3030.0000.0000.1740.000
altura_mencionada-0.2870.4700.2180.8781.0000.2910.0340.1770.2380.2090.3150.0000.2060.0000.0760.3210.2600.000-0.1230.2270.0000.0000.0000.2450.4970.0000.019-0.1090.2320.0000.3180.0000.3810.6480.0000.3330.2530.2800.2980.470-0.067-0.089-0.1840.1360.2450.0000.1970.5620.1760.1570.0000.280-0.3090.2690.3750.0000.0000.1500.1760.1760.1570.252
artritis_reumatoide0.0000.0970.0000.0000.2911.0000.3280.0000.2810.0000.0000.0000.0000.0000.0000.0000.0000.0000.3550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2530.0000.0000.4630.0000.0000.0000.3260.0000.0000.0910.0000.4540.0000.0000.0000.0000.4280.0000.0000.4850.0000.0000.4200.0000.1930.0000.0000.0000.176
cadera_total0.1230.0660.0000.1780.0340.3281.0000.2270.4560.0000.1990.2350.2810.3080.3570.0800.5250.447-0.3250.1870.4670.1230.0000.3200.2260.0000.5060.1680.1980.4680.0000.0000.2870.2840.0000.7070.0000.2720.4030.202-0.402-0.385-0.1270.5290.6650.3370.2990.2260.3650.3900.3340.215-0.2790.2480.0000.1660.0000.0000.3650.3650.2650.929
caidas0.2560.3260.2160.3660.1770.0000.2271.0000.0000.0000.3120.1650.0000.0000.0670.0000.0610.1070.0000.0860.1400.2120.0000.0000.0000.0000.0000.0000.0000.1220.0000.0000.0000.1180.0000.0000.0380.1430.5290.0000.0000.0000.1260.2130.0000.0000.2440.0480.0000.0000.1350.0000.4690.0000.0000.0000.3310.0000.0000.0000.0000.000
caminata0.4270.3350.1610.0000.2380.2810.4560.0001.0000.3020.2320.3130.2100.3400.3260.3600.0000.4470.5220.4440.2150.0000.0000.2590.2260.0000.1900.3130.3460.0530.4020.0000.3710.1180.1220.1360.0000.2740.0000.3980.0000.0000.4320.3030.5620.6150.5120.4000.4320.5570.2610.5990.6560.6340.3760.3210.0000.1840.4320.4320.5170.679
capacidad_de_usar_el_telefono0.3110.2030.0000.0000.2090.0000.0000.0000.3021.0000.0000.2340.0000.2650.4100.2360.0000.0000.0000.1430.0000.0000.1470.2960.3920.0000.0000.2900.0000.0000.3130.0000.2010.0000.0000.0000.0000.2430.0000.0000.4030.2990.1400.2120.0000.3940.2300.2150.0000.6840.1740.2300.2720.2950.0000.0750.0000.0000.0000.0000.3430.000
circunferencia_de_la_pantorrilla0.384-0.0570.2480.3440.3150.0000.1990.3120.2320.0001.0000.5130.0000.0000.1820.0000.8340.000-0.1480.2100.2070.0000.1580.2240.2170.0000.4490.6720.7710.4980.0000.0000.2050.7480.0000.0000.0000.2670.1910.636-0.384-0.394-0.1180.4450.2490.2790.0840.3270.2620.0000.0600.082-0.0270.2210.1050.0000.4070.0000.2620.262-0.1290.108
clas_imc0.6220.4470.1160.1630.0000.0000.2350.1650.3130.2340.5131.0000.0000.0000.0800.2800.3530.3360.0000.1280.1900.0000.0000.0000.0000.0000.4210.8190.2820.2760.2560.0000.3130.1280.1170.0000.0000.0810.0000.4020.0600.0680.1580.1480.2980.2950.3020.1690.4270.2460.2430.2690.1620.3530.0500.0790.3750.0000.4270.4270.2660.331
clasificacion_de_densidad_mineral0.2670.0000.0000.0000.2060.0000.2810.0000.2100.0000.0000.0001.0000.2230.6980.0000.0000.0000.0000.0000.7930.0000.0000.0000.0000.0000.1600.0540.0710.2660.0000.0000.0000.3060.0000.6170.2620.2150.0000.4100.1670.2120.0600.1810.0810.1570.0000.0000.0000.0000.0000.0000.1230.0000.0000.0000.0000.0000.0000.0000.1120.000
clasificacion_de_estado_fisico0.2680.1100.0000.2650.0000.0000.3080.0000.3400.2650.0000.0000.2231.0000.1060.3870.2850.3620.3130.4170.1460.1960.0000.4030.5430.1960.1750.2820.3570.1340.4280.1240.4840.3520.0000.1830.3500.4660.0000.2080.0950.2350.6000.5940.1580.6020.8520.5930.2230.3130.5900.7520.4600.7000.5190.3470.3500.2630.2230.2230.4840.124
columna0.225-0.1320.0000.1650.0760.0000.3570.0670.3260.4100.1820.0800.6980.1061.0000.0960.0000.487-0.0170.3200.4940.0000.1610.000-0.0470.0000.0000.2790.3190.2360.0570.3210.0000.3190.1490.3330.0000.0000.0000.280-0.119-0.109-0.0820.0000.1570.1780.0050.0000.2670.4820.0000.0490.0300.0000.2210.0000.0700.0950.2670.267-0.0920.000
compras0.1130.0000.0000.0000.3210.0000.0800.0000.3600.2360.0000.2800.0000.3870.0961.0000.0000.0810.0000.1390.2570.0000.0000.3430.2920.0000.1030.3580.0000.0000.7330.0000.3900.2170.0000.0000.0000.3860.0000.0000.0000.0000.3700.4270.4880.5330.5070.3690.4290.8120.3470.2930.5120.2980.2750.4260.0000.2040.4290.4290.4040.000
cp_sarcopenia0.4400.0000.0000.2640.2600.0000.5250.0610.0000.0000.8340.3530.0000.2850.0000.0001.0000.0000.0050.0000.3530.0000.0000.0000.2670.0000.5180.4670.6720.7080.0000.0000.4760.5510.0000.0000.1000.4690.0000.5790.5930.4660.1060.5060.0000.4000.2920.2420.0000.0000.3550.1160.0000.0000.0000.0000.0000.0000.0000.0000.0460.000
cuidado_del_hogar0.0000.0770.0000.0000.0000.0000.4470.1070.4470.0000.0000.3360.0000.3620.4870.0810.0001.0000.0000.0570.0000.0000.0000.2480.2180.0000.0000.2280.5890.0000.2330.0000.3200.5680.0000.0000.0000.1760.0000.5500.2260.3200.1820.1850.5830.3610.3500.0000.2680.7950.0370.1560.5410.2170.0780.2410.0000.0000.2680.2680.5420.229
edad-0.071-0.1040.163-0.212-0.1230.355-0.3250.0000.5220.000-0.1480.0000.0000.313-0.0170.0000.0050.0001.0000.2500.2780.0000.0000.132-0.2410.1480.268-0.027-0.0240.0000.0000.0000.196-0.1790.3770.0000.0000.1820.191-0.1490.7850.6320.3660.2330.1380.395-0.5130.3430.0000.0000.0000.2070.3720.3300.2970.0000.0000.2360.0000.000-0.4810.108
ejercicio0.2590.4870.0000.4740.2270.0000.1870.0860.4440.1430.2100.1280.0000.4170.3200.1390.0000.0570.2501.0000.0000.0000.0000.5880.6420.0000.4090.3460.2810.1310.1510.0000.6550.3280.0000.0000.2080.5930.0000.0000.2630.2820.3260.4850.1090.5680.4220.6270.0780.1400.3680.5140.6500.6290.5340.3960.0000.9310.0780.0780.4760.000
estratificacion_de_riesgo0.3060.0000.0000.0800.0000.0000.4670.1400.2150.0000.2070.1900.7930.1460.4940.2570.3530.0000.2780.0001.0000.5700.2970.0000.1550.1140.2120.2300.2850.2390.0870.1600.2910.2610.0000.0000.3100.1160.0000.4140.4450.3760.0000.2560.0000.0980.0000.0000.0000.1540.0950.0000.2150.0000.0000.0000.1360.1410.0000.0000.0000.160
finanzas0.0000.0000.0000.0000.0000.0000.1230.2120.0000.0000.0000.0000.0000.1960.0000.0000.0000.0000.0000.0000.5701.0000.0000.2080.0000.0000.0000.2040.3280.0000.1190.0000.0000.0000.0001.0000.0000.0000.0000.2200.0000.0000.0000.2320.0000.0000.0000.0000.0000.1820.0000.0000.0000.0000.0000.0000.0000.1800.0000.0000.0000.000
fracturas_previas0.0000.3160.0000.0000.0000.0000.0000.0000.0000.1470.1580.0000.0000.0000.1610.0000.0000.0000.0000.0000.2970.0001.0000.0000.2070.0000.0000.0000.0000.0000.0380.0000.0000.0850.1200.0000.0820.0000.0000.0000.5560.6620.0000.0000.0000.0000.2590.0000.0000.2030.0000.0000.0000.0000.0000.1520.0000.0000.0000.0000.0000.000
fuerza0.2370.0000.0000.2830.2450.0000.3200.0000.2590.2960.2240.0000.0000.4030.0000.3430.0000.2480.1320.5880.0000.2080.0001.0000.4490.0000.2110.0000.3930.2740.3930.0780.4460.4150.1340.0000.2550.3450.0000.2980.1960.2300.4640.4020.2470.3650.3790.7550.0000.3820.3570.4600.4950.6520.4690.3540.0000.3870.0000.0000.2030.386
fuerza_de_prension-0.2310.3970.1460.4340.4970.0000.2260.0000.2260.3920.2170.0000.0000.543-0.0470.2920.2670.218-0.2410.6420.1550.0000.2070.4491.0000.0000.247-0.1940.0610.0000.3860.3880.3220.2870.0000.3330.5020.3000.0000.205-0.247-0.245-0.6230.4370.0000.3620.6780.9180.0000.1390.2000.549-0.6980.6640.4260.0780.1510.3450.0000.0000.5140.000
glucocorticoides0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1960.0000.0000.0000.0000.1480.0000.1140.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.4480.0000.0001.0000.0000.0000.0000.0000.1930.2820.0000.0460.1950.0000.0660.0000.0000.0000.0000.0000.4690.0000.0000.6710.0000.1800.0000.0000.0000.000
grasa_resultado0.8840.5860.0000.4340.0190.0000.5060.0000.1900.0000.4490.4210.1600.1750.0000.1030.5180.0000.2680.4090.2120.0000.0000.2110.2470.0001.0000.4390.4090.5020.2310.0000.3360.2680.4340.0000.0000.1680.0000.4670.3300.3440.0000.5760.0000.1780.3570.3450.0000.0000.3640.2840.0000.3150.2800.0000.2310.2420.0000.0000.0990.000
imc0.795-0.5690.491-0.111-0.1090.0000.1680.0000.3130.2900.6720.8190.0540.2820.2790.3580.4670.228-0.0270.3460.2300.2040.0000.000-0.1940.0000.4391.0000.7220.1650.3860.0000.4920.4590.1410.3330.1720.1780.0000.652-0.359-0.3320.1940.2610.3170.385-0.2150.1020.5070.2510.3490.4220.3690.5450.2840.3730.3910.1510.5070.507-0.3920.289
imme0.384-0.0150.0000.2450.2320.0000.1980.0000.3460.0000.7710.2820.0710.3570.3190.0000.6720.589-0.0240.2810.2850.3280.0000.3930.0610.0000.4090.7221.0000.8600.3570.2660.4560.8300.0000.0000.0000.4740.0000.673-0.289-0.2800.0610.4450.3770.226-0.0320.3910.2610.2740.1950.4020.1340.4620.0740.3280.0720.1700.2610.261-0.1750.457
imme_resultado0.4060.2050.1840.1540.0000.0000.4680.1220.0530.0000.4980.2760.2660.1340.2360.0000.7080.0000.0000.1310.2390.0000.0000.2740.0000.0000.5020.1650.8601.0000.0000.2670.3190.5950.3050.0000.0000.4480.0000.4570.4810.4780.1920.5030.0000.1950.2440.2560.0000.0000.0000.1580.0000.0280.1590.0000.0000.0000.0000.0000.1120.000
interpretacion_lawton0.0200.0000.0000.0000.3180.0000.0000.0000.4020.3130.0000.2560.0000.4280.0570.7330.0000.2330.0000.1510.0870.1190.0380.3930.3860.0000.2310.3860.3570.0001.0000.0000.3930.3730.0000.0000.0000.5030.0640.2920.0000.0000.4620.3760.4860.4680.4270.3980.4390.9250.4720.3400.4770.3830.2800.3860.0000.2870.4390.4390.3210.000
lavanderia0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1240.3210.0000.0000.0000.0000.0000.1600.0000.0000.0780.3880.0000.0000.0000.2660.2670.0001.0000.0000.0000.0001.0000.0000.4260.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0000.2250.0000.0000.3210.0000.0000.0000.0000.0000.0000.0000.0000.000
levantarse0.3620.3600.0000.0000.3810.2530.2870.0000.3710.2010.2050.3130.0000.4840.0000.3900.4760.3200.1960.6550.2910.0000.0000.4460.3220.4480.3360.4920.4560.3190.3930.0001.0000.3630.0000.0000.1630.3790.0000.1660.5400.6720.4400.5240.2250.5500.6880.6360.3680.2220.6520.5860.6910.9040.5490.4880.3040.3760.3680.3680.5400.078
masa_muscular_absoluta0.1100.2460.0000.6880.6480.0000.2840.1180.1180.0000.7480.1280.3060.3520.3190.2170.5510.568-0.1790.3280.2610.0000.0850.4150.2870.0000.2680.4590.8300.5950.3730.0000.3631.0000.0000.4710.1240.3540.0000.737-0.340-0.343-0.0200.2910.2950.1790.0920.3880.0000.2370.0000.321-0.0850.3890.1450.2760.0000.2640.0000.000-0.0280.457
medicamento_10.3120.2470.3340.5540.0000.0000.0000.0000.1220.0000.0000.1170.0000.0000.1490.0000.0000.0000.3770.0000.0000.0000.1200.1340.0000.0000.4340.1410.0000.3050.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.1690.2400.0000.0000.0000.0000.2680.0000.0000.0000.0000.0000.1420.0000.0000.2360.6550.0000.0000.0000.1880.000
medicamento_20.0000.0001.0000.0000.3330.4630.7070.0000.1360.0000.0000.0000.6170.1830.3330.0000.0000.0000.0000.0000.0001.0000.0000.0000.3331.0000.0000.3330.0000.0000.0001.0000.0000.4711.0001.0001.0000.0001.0000.3330.0000.0000.0000.5900.1360.1670.5270.2930.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.5610.463
medicamentos0.0000.1910.0000.0980.2530.0000.0000.0380.0000.0000.0000.0000.2620.3500.0000.0000.1000.0000.0000.2080.3100.0000.0820.2550.5020.0000.0000.1720.0000.0000.0000.0000.1630.1240.0001.0001.0000.0000.0000.1030.2090.1800.2390.1770.0000.1990.2190.0000.0000.0000.1520.0000.0000.1510.0000.1540.0000.1520.0000.0000.0000.000
nivel_de_sarcopenia0.1700.2270.1650.2910.2800.0000.2720.1430.2740.2430.2670.0810.2150.4660.0000.3860.4690.1760.1820.5930.1160.0000.0000.3450.3000.0000.1680.1780.4740.4480.5030.4260.3790.3540.0000.0000.0001.0000.0000.2680.1000.1370.2430.3410.2800.3960.3080.6820.2740.3200.2230.6640.3370.5260.9420.2120.0000.2950.2740.2740.3350.192
padres_con_fractura_de_cadera0.0000.0000.0000.3220.2980.0000.4030.5290.0000.0000.1910.0000.0000.0000.0000.0000.0000.0000.1910.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0001.0000.0000.0001.0000.2610.0000.2550.0000.3250.0000.0000.0000.0000.0000.0000.3020.0000.1890.0000.0000.0000.0000.0000.0000.0000.0000.000
peso0.394-0.0420.0000.4240.4700.3260.2020.0000.3980.0000.6360.4020.4100.2080.2800.0000.5790.550-0.1490.0000.4140.2200.0000.2980.2050.0000.4670.6520.6730.4570.2920.0000.1660.7370.0000.3330.1030.2680.2611.000-0.320-0.266-0.0630.3660.6270.3740.1440.2490.2560.4410.1250.247-0.0130.3850.0000.1030.2820.0960.2560.256-0.0310.918
probabilida_de_fractura_de_cadera-0.3220.1090.000-0.162-0.0670.000-0.4020.0000.0000.403-0.3840.0600.1670.095-0.1190.0000.5930.2260.7850.2630.4450.0000.5560.196-0.2470.1930.330-0.359-0.2890.4810.0000.0000.540-0.3400.1690.0000.2090.1000.000-0.3201.0000.9380.2950.2830.0000.131-0.3920.2110.0400.2090.1560.2050.2800.3260.1930.1010.1450.0000.0400.040-0.3050.000
probabilida_de_fractura_por_fragilidad-0.2710.0970.186-0.166-0.0890.000-0.3850.0000.0000.299-0.3940.0680.2120.235-0.1090.0000.4660.3200.6320.2820.3760.0000.6620.230-0.2450.2820.344-0.332-0.2800.4780.0000.0000.672-0.3430.2400.0000.1800.1370.255-0.2660.9381.0000.1980.3740.0000.230-0.2660.0000.0000.1540.1470.2850.2600.2460.1610.0720.2840.0000.0000.000-0.2070.000
prueba_de_la_silla0.128-0.2610.230-0.178-0.1840.091-0.1270.1260.4320.140-0.1180.1580.0600.600-0.0820.3700.1060.1820.3660.3260.0000.0000.0000.464-0.6230.0000.0000.1940.0610.1920.4620.0000.440-0.0200.0000.0000.2390.2430.000-0.0630.2950.1981.0000.3360.3510.329-0.8680.5980.3510.4410.7740.4540.7500.6490.4450.3550.1770.1560.3510.351-0.5900.283
puntaje_de_balance0.5920.4360.0000.0000.1360.0000.5290.2130.3030.2120.4450.1480.1810.5940.0000.4270.5060.1850.2330.4850.2560.2320.0000.4020.4370.0460.5760.2610.4450.5030.3760.0000.5240.2910.0000.5900.1770.3410.3250.3660.2830.3740.3361.0000.2030.3910.5890.6930.3740.1600.5030.6580.3510.6890.4210.2920.2610.1910.3740.3740.3400.000
puntaje_de_katz0.3580.3100.0000.0000.2450.4540.6650.0000.5620.0000.2490.2980.0810.1580.1570.4880.0000.5830.1380.1090.0000.0000.0000.2470.0000.1950.0000.3170.3770.0000.4860.0000.2250.2950.0000.1360.0000.2800.0000.6270.0000.0000.3510.2031.0000.4330.2860.1770.9900.8150.0000.2390.7220.3240.1340.3850.3490.0000.9900.9900.2900.990
puntaje_de_velocidad_de_marcha0.3200.2620.0790.0000.0000.0000.3370.0000.6150.3940.2790.2950.1570.6020.1780.5330.4000.3610.3950.5680.0980.0000.0000.3650.3620.0000.1780.3850.2260.1950.4680.0390.5500.1790.0000.1670.1990.3960.0000.3740.1310.2300.3290.3910.4331.0000.7090.5700.5780.3480.3540.8410.5290.6990.6300.3650.4060.2600.5780.5780.7530.426
puntaje_sppb-0.1960.3430.0050.1830.1970.0000.2990.2440.5120.2300.0840.3020.0000.8520.0050.5070.2920.350-0.5130.4220.0000.0000.2590.3790.6780.0660.357-0.215-0.0320.2440.4270.0000.6880.0920.2680.5270.2190.3080.0000.144-0.392-0.266-0.8680.5890.2860.7091.0000.5650.4890.1500.8520.736-0.7610.7390.4970.3470.2300.2940.4890.4890.8090.108
resultado_de_fp0.5110.5220.0000.3650.5620.0000.2260.0480.4000.2150.3270.1690.0000.5930.0000.3690.2420.0000.3430.6270.0000.0000.0000.7550.9180.0000.3450.1020.3910.2560.3980.0000.6360.3880.0000.2930.0000.6820.0000.2490.2110.0000.5980.6930.1770.5700.5651.0000.0000.4850.5300.5240.7490.6910.5020.3540.0000.6750.0000.0000.4530.000
resultado_de_katz0.2930.1800.0000.0000.1760.0000.3650.0000.4320.0000.2620.4270.0000.2230.2670.4290.0000.2680.0000.0780.0000.0000.0000.0000.0000.0000.0000.5070.2610.0000.4390.0000.3680.0000.0000.0000.0000.2740.0000.2560.0400.0000.3510.3740.9900.5780.4890.0001.0000.6700.1020.2010.5010.2800.1030.4100.1930.0670.9040.9040.4650.098
resultado_de_lawton0.1710.2130.0000.0000.1570.4280.3900.0000.5570.6840.0000.2460.0000.3130.4820.8120.0000.7950.0000.1400.1540.1820.2030.3820.1390.0000.0000.2510.2740.0000.9250.2250.2220.2370.0000.0000.0000.3200.0000.4410.2090.1540.4410.1600.8150.3480.1500.4850.6701.0000.1220.3990.5210.4300.3090.3850.1600.0000.6700.6700.2340.970
resultado_de_ps0.3170.2640.0000.0000.0000.0000.3340.1350.2610.1740.0600.2430.0000.5900.0000.3470.3550.0370.0000.3680.0950.0000.0000.3570.2000.0000.3640.3490.1950.0000.4720.0000.6520.0000.0000.0000.1520.2230.3020.1250.1560.1470.7740.5030.0000.3540.8520.5300.1020.1221.0000.5680.3100.6650.4420.3380.1740.2720.1020.1020.4100.000
resultado_de_velocidad0.4630.5150.1380.2600.2800.0000.2150.0000.5990.2300.0820.2690.0000.7520.0490.2930.1160.1560.2070.5140.0000.0000.0000.4600.5490.0000.2840.4220.4020.1580.3400.0000.5860.3210.0000.0000.0000.6640.0000.2470.2050.2850.4540.6580.2390.8410.7360.5240.2010.3990.5681.0000.6330.6400.5460.3120.0000.5560.2010.2010.6950.000
sarc_f_puntaje0.298-0.3850.000-0.306-0.3090.485-0.2790.4690.6560.272-0.0270.1620.1230.4600.0300.5120.0000.5410.3720.6500.2150.0000.0000.495-0.6980.4690.0000.3690.1340.0000.4770.3210.691-0.0850.1420.0000.0000.3370.189-0.0130.2800.2600.7500.3510.7220.529-0.7610.7490.5010.5210.3100.6331.0000.9390.4960.7640.1970.1630.5010.501-0.6300.939
sarc_f_resultado0.5480.4450.0000.2250.2690.0000.2480.0000.6340.2950.2210.3530.0000.7000.0000.2980.0000.2170.3300.6290.0000.0000.0000.6520.6640.0000.3150.5450.4620.0280.3830.0000.9040.3890.0000.0000.1510.5260.0000.3850.3260.2460.6490.6890.3240.6990.7390.6910.2800.4300.6650.6400.9391.0000.4050.5760.1030.5960.2800.2800.5730.000
sarcopenia0.1230.3230.0000.4400.3750.0000.0000.0000.3760.0000.1050.0500.0000.5190.2210.2750.0000.0780.2970.5340.0000.0000.0000.4690.4260.0000.2800.2840.0740.1590.2800.0000.5490.1450.0000.0000.0000.9420.0000.0000.1930.1610.4450.4210.1340.6300.4970.5020.1030.3090.4420.5460.4960.4051.0000.3740.0000.5950.1030.1030.5400.000
subir_escaleras0.0000.0000.0000.1920.0000.4200.1660.0000.3210.0750.0000.0790.0000.3470.0000.4260.0000.2410.0000.3960.0000.0000.1520.3540.0780.6710.0000.3730.3280.0000.3860.0000.4880.2760.2360.0000.1540.2120.0000.1030.1010.0720.3550.2920.3850.3650.3470.3540.4100.3850.3380.3120.7640.5760.3741.0000.0940.2100.4100.4100.2920.448
tabaquismo0.5290.3920.0000.4380.0000.0000.0000.3310.0000.0000.4070.3750.0000.3500.0700.0000.0000.0000.0000.0000.1360.0000.0000.0000.1510.0000.2310.3910.0720.0000.0000.0000.3040.0000.6551.0000.0000.0000.0000.2820.1450.2840.1770.2610.3490.4060.2300.0000.1930.1600.1740.0000.1970.1030.0000.0941.0000.0000.1930.1930.1800.000
tiempo_de_ejercicio0.1800.2110.0000.3030.1500.1930.0000.0000.1840.0000.0000.0000.0000.2630.0950.2040.0000.0000.2360.9310.1410.1800.0000.3870.3450.1800.2420.1510.1700.0000.2870.0000.3760.2640.0000.0000.1520.2950.0000.0960.0000.0000.1560.1910.0000.2600.2940.6750.0670.0000.2720.5560.1630.5960.5950.2100.0001.0000.0670.0670.0420.000
transferencia0.2930.1800.0000.0000.1760.0000.3650.0000.4320.0000.2620.4270.0000.2230.2670.4290.0000.2680.0000.0780.0000.0000.0000.0000.0000.0000.0000.5070.2610.0000.4390.0000.3680.0000.0000.0000.0000.2740.0000.2560.0400.0000.3510.3740.9900.5780.4890.0000.9040.6700.1020.2010.5010.2800.1030.4100.1930.0671.0000.9040.4650.098
transporte0.2930.1800.0000.0000.1760.0000.3650.0000.4320.0000.2620.4270.0000.2230.2670.4290.0000.2680.0000.0780.0000.0000.0000.0000.0000.0000.0000.5070.2610.0000.4390.0000.3680.0000.0000.0000.0000.2740.0000.2560.0400.0000.3510.3740.9900.5780.4890.0000.9040.6700.1020.2010.5010.2800.1030.4100.1930.0670.9041.0000.4650.098
velocidad_de_la_marcha-0.3360.4480.1120.1740.1570.0000.2650.0000.5170.343-0.1290.2660.1120.484-0.0920.4040.0460.542-0.4810.4760.0000.0000.0000.2030.5140.0000.099-0.392-0.1750.1120.3210.0000.540-0.0280.1880.5610.0000.3350.000-0.031-0.305-0.207-0.5900.3400.2900.7530.8090.4530.4650.2340.4100.695-0.6300.5730.5400.2920.1800.0420.4650.4651.0000.226
vestido0.3880.3880.0000.0000.2520.1760.9290.0000.6790.0000.1080.3310.0000.1240.0000.0000.0000.2290.1080.0000.1600.0000.0000.3860.0000.0000.0000.2890.4570.0000.0000.0000.0780.4570.0000.4630.0000.1920.0000.9180.0000.0000.2830.0000.9900.4260.1080.0000.0980.9700.0000.0000.9390.0000.0000.4480.0000.0000.0980.0980.2261.000

Missing values

2025-05-02T12:22:04.859651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-02T12:22:05.065079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-02T12:22:05.241944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

edadpesoaltura_en_cmaltura_mencionadaaltura_correctaimcclas_imccircunferencia_de_la_pantorrillacp_sarcopenia%_de_grasagrasa_resultado%_de_musculomasa_muscular_absolutaimmeimme_resultadocadera_totalcolumnaclasificacion_de_densidad_mineralfracturas_previaspadres_con_fractura_de_caderatabaquismoglucocorticoidesartritis_reumatoideosteoporosis_secalcoholprobabilida_de_fractura_por_fragilidadprobabilida_de_fractura_de_caderaestratificacion_de_riesgofuerza_de_prensionresultado_de_fpprueba_de_la_sillaresultado_de_pspuntaje_de_balancevelocidad_de_la_marcharesultado_de_velocidadpuntaje_de_velocidad_de_marchapuntaje_sppbclasificacion_de_estado_fisicofuerzacaminatalevantarsesubir_escalerascaidassarc_f_puntajesarc_f_resultadocapacidad_de_usar_el_telefonotransportemedicacionfinanzascomprascocinacuidado_del_hogarlavanderiaresultado_de_lawtoninterpretacion_lawtonbañovestidosanitariotransferenciacontinenciaalimentacionpuntaje_de_katzresultado_de_katzejerciciotiempo_de_ejerciciomedicamentosmedicamento_1medicamento_2sarcopenianivel_de_sarcopenia
055.081.9162.0168.0no31.2obesidad 136.0no40.8peligrosamente alta26.521.708.28normal-1.1-1.6osteopenianonononosinono3.10.4baja24.1normal18.001.04.00.94normal4.09.0intermedioningunaningunaningunapocaninguna1.0baja probabilidad de sarcopeniasisisinosisisisi7.0deteriro funcionalsisisisisisi6.0independencia totalsi30 - 60 minssiantidiabeticosNaNnosin sarcopenia
156.066.3151.0152.0no29.1sobrepeso34.0no40.6peligrosamente alta26.017.247.56normal-1.7-2.7osteoporosisnononosinonono4.20.6alta22.0normal15.502.02.00.85normal4.08.0intermedioningunaningunaningunamuchaninguna2.0baja probabilidad de sarcopeniasinosisinosisisi6.0deteriro funcionalsisisinosisi5.0deterioro funcionalsi30 - 60 minssiantidiabeticosNaNnosin sarcopenia
278.059.3152.0157.0no25.7sobrepeso36.0no33.7alta28.617.567.60normal-1.3-2.3osteoporosissinononononono18.06.6muy alta22.0normal15.502.02.00.84normal4.08.0intermedioningunaningunaningunaningunaninguna0.0baja probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalsi121 - 180 minssiantihipertensivosNaNnosin sarcopenia
360.061.4150.0151.0no27.3sobrepeso36.0no39.0peligrosamente alta26.216.097.15normal-1.3-3.1osteoporosissinononononono7.21.4muy alta21.7normal12.822.04.01.12normal4.010.0altoningunaningunaningunaningunaninguna0.0baja probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalsi121 - 180 minssiantidiabeticosNaNnosin sarcopenia
478.082.4152.0154.0no35.7obesidad 237.0no45.5peligrosamente alta24.420.118.70normal-1.70.5osteopeniasinononononono13.04.0muy alta18.0sarcopenia21.121.02.00.62sarcopenia2.05.0bajopocapocapocapocaninguna4.0alta probabilidad de sarcopenianonosisinosinosi4.0deteriro funcionalsisisinosisi5.0deterioro funcionalnonosiantihipertensivosantidiabeticossisarcopenia moderada
564.072.6158.0160.0no29.1sobrepeso38.0no41.9peligrosamente alta25.118.227.13normal-0.6-1.8osteopenianonononononono4.30.7moderada24.5normal15.502.04.01.12normal4.010.0altoningunaningunaningunaninguna1 - 3 caidas1.0baja probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalsi121 - 180 minsnoNaNNaNnosin sarcopenia
659.077.5147.0147.0si35.9obesidad 233.0no51.5peligrosamente alta21.016.287.53normal0.5-1.1osteopenianosinonononono5.00.4baja20.7normal13.473.04.00.88normal4.011.0altoningunaningunaningunapoca4 o mas caidas3.0baja probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalnonosiantidiabeticosNaNnosin sarcopenia
768.085.4152.0154.0no37.0obesidad 241.0no46.1peligrosamente alta24.420.409.02normal-1.1-1.2osteopeniasinononononono8.51.9baja16.7sarcopenia19.561.03.00.75sarcopenia3.07.0intermediomuchapocaningunaninguna4 o mas caidas5.0alta probabilidad de sarcopenianosisinosisisisi6.0deteriro funcionalsisisisisisi6.0independencia totalnonosiNaNNaNsisarcopenia moderada
873.062.5143.0140.0no30.6obesidad 137.0no42.4peligrosamente alta25.115.697.69normal-1.8-2.4osteopenianonononononono7.42.1moderada25.1normal13.912.04.00.83normal4.010.0altoningunapocaningunapoca1 - 3 caidas3.0baja probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalsi121 - 180 minsnoNaNNaNsisin sarcopenia
959.081.4169.0168.0no28.5sobrepeso38.0no39.9peligrosamente alta26.321.417.51normal-1.5-1.6osteopenianonononononono3.20.4moderada28.0normal12.663.04.00.90normal4.011.0altoningunaningunaningunaningunaninguna0.0baja probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalsimas de 180 minssiantihipertensivosNaNnosin sarcopenia
edadpesoaltura_en_cmaltura_mencionadaaltura_correctaimcclas_imccircunferencia_de_la_pantorrillacp_sarcopenia%_de_grasagrasa_resultado%_de_musculomasa_muscular_absolutaimmeimme_resultadocadera_totalcolumnaclasificacion_de_densidad_mineralfracturas_previaspadres_con_fractura_de_caderatabaquismoglucocorticoidesartritis_reumatoideosteoporosis_secalcoholprobabilida_de_fractura_por_fragilidadprobabilida_de_fractura_de_caderaestratificacion_de_riesgofuerza_de_prensionresultado_de_fpprueba_de_la_sillaresultado_de_pspuntaje_de_balancevelocidad_de_la_marcharesultado_de_velocidadpuntaje_de_velocidad_de_marchapuntaje_sppbclasificacion_de_estado_fisicofuerzacaminatalevantarsesubir_escalerascaidassarc_f_puntajesarc_f_resultadocapacidad_de_usar_el_telefonotransportemedicacionfinanzascomprascocinacuidado_del_hogarlavanderiaresultado_de_lawtoninterpretacion_lawtonbañovestidosanitariotransferenciacontinenciaalimentacionpuntaje_de_katzresultado_de_katzejerciciotiempo_de_ejerciciomedicamentosmedicamento_1medicamento_2sarcopenianivel_de_sarcopenia
4370.054.4156.0158.0no22.7adecuado31.0si29.2normal29.916.276.69sarcopenia grado 1-1.5-2.8osteoporosisnonononononono7.82.7alta29.4normal18.851.02.00.77sarcopenia3.06.0bajoningunaningunaningunaninguna1 - 3 caidas1.0baja probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalsi121 - 180 minssiantihipertensivosantidiabeticossisarcopenia moderada
4476.049.0137.0140.0no26.1sobrepeso30.0si37.0peligrosamente alta22.110.835.79sarcopenia grado 1-4.2-2.5osteoporosisnosinonononono18.012.0muy alta16.2sarcopenia19.841.02.00.63sarcopenia2.05.0bajoningunaningunapocapoca4 o mas caidas4.0alta probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalnonosiantihipertensivosNaNsisarcopenia severa
4562.076.7168.0168.0si27.2sobrepeso34.0no38.8peligrosamente alta26.520.337.20normal-0.8-2.0osteopeniasinononononono8.11.7moderada20.6normal15.432.04.00.92normal4.010.0altoningunaningunaningunaningunaninguna0.0baja probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalsi121 - 180 minsnoNaNNaNnosin sarcopenia
4664.060.3146.0147.0no27.9sobrepeso28.0si40.7peligrosamente alta25.316.197.49normal-0.7-1.9osteopenianonononononono4.40.8moderada15.7sarcopenia21.401.04.00.90normal3.08.0intermediopocaningunaningunapocaninguna2.0baja probabilidad de sarcopeniasisisisinosisisi7.0deteriro funcionalsisisisisisi6.0independencia totalsi121 - 180 minssiantidiabeticosNaNsisarcopenia leve
4769.056.0147.0150.0no25.9sobrepeso33.0no38.8peligrosamente alta25.414.226.58sarcopenia grado 1-0.3-2.8osteoporosisnonononononono6.61.7alta23.1normal15.192.04.00.91normal4.010.0altoningunaningunaningunapoca1 - 3 caidas2.0baja probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalnonosiantidiabeticosNaNsisarcopenia leve
4870.090.0159.0160.0no35.6obesidad 241.0no49.3peligrosamente alta22.920.618.17normal-0.8-1.3osteopenianonononononono5.21.1moderada31.9normal14.402.02.00.80sarcopenia3.07.0intermedioningunaningunaningunapocaninguna1.0baja probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalsimas de 180 minsnoNaNNaNsisarcopenia leve
4968.075.0160.0165.0no29.3sobrepeso36.0no37.6peligrosamente alta27.820.858.14normal-2.0-3.8osteoporosissinononononono11.02.8alta23.0normal19.501.04.00.46normal2.07.0intermediopocaningunapocapocaninguna3.0baja probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalnonosiantidiabeticosNaNsisarcopenia leve
5064.066.7154.0152.0no28.1sobrepeso32.5no42.6peligrosamente alta24.116.076.78normal-0.7-1.2osteopeniasinononononono8.82.0moderada26.4normal15.282.04.00.77sarcopenia3.09.0intermedioningunaningunaningunaningunaninguna0.0baja probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalnonosiantihipertensivosantidiabeticossisarcopenia leve
5161.081.6154.0154.0si34.4obesidad 134.0no46.6peligrosamente alta23.719.378.17normal-1.7-3.3osteoporosisnosinonononono5.90.5alta19.0sarcopenia16.502.03.00.94normal4.09.0intermediopocaningunapocapoca1 - 3 caidas4.0alta probabilidad de sarcopeniasisisisisisisisi8.0independencia totalsisisisisisi6.0independencia totalnonosiantidiabeticosNaNsisarcopenia leve
5265.065.0160.0155.0no45.7obesidad 344.0no57.0peligrosamente alta19.120.958.72normal-0.6-1.3osteopenianonosinononono3.10.5moderada18.8sarcopenia18.301.02.00.34sarcopenia1.04.0intermedioningunapocapocapoca1 - 3 caidas4.0alta probabilidad de sarcopeniasinosisinosisisi6.0deteriro funcionalsisisinosisi5.0deterioro funcionalnononoNaNNaNsisarcopenia moderada

Duplicate rows

Most frequently occurring

edadpesoaltura_en_cmaltura_mencionadaaltura_correctaimcclas_imccircunferencia_de_la_pantorrillacp_sarcopenia%_de_grasagrasa_resultado%_de_musculomasa_muscular_absolutaimmeimme_resultadocadera_totalcolumnaclasificacion_de_densidad_mineralfracturas_previaspadres_con_fractura_de_caderatabaquismoglucocorticoidesartritis_reumatoideosteoporosis_secalcoholprobabilida_de_fractura_por_fragilidadprobabilida_de_fractura_de_caderaestratificacion_de_riesgofuerza_de_prensionresultado_de_fpprueba_de_la_sillaresultado_de_pspuntaje_de_balancevelocidad_de_la_marcharesultado_de_velocidadpuntaje_de_velocidad_de_marchapuntaje_sppbclasificacion_de_estado_fisicofuerzacaminatalevantarsesubir_escalerascaidassarc_f_puntajesarc_f_resultadocapacidad_de_usar_el_telefonotransportemedicacionfinanzascomprascocinacuidado_del_hogarlavanderiaresultado_de_lawtoninterpretacion_lawtonbañovestidosanitariotransferenciacontinenciaalimentacionpuntaje_de_katzresultado_de_katzejerciciotiempo_de_ejerciciomedicamentosmedicamento_1medicamento_2sarcopenianivel_de_sarcopenia# duplicates
065.065.0160.0155.0no45.7obesidad 344.0no57.0peligrosamente alta19.120.958.72normal-0.6-1.3osteopenianonosinononono3.10.5moderada18.8sarcopenia18.31.02.00.34sarcopenia1.04.0intermedioningunapocapocapoca1 - 3 caidas4.0alta probabilidad de sarcopeniasinosisinosisisi6.0deteriro funcionalsisisinosisi5.0deterioro funcionalnononoNaNNaNsisarcopenia moderada2